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Rapid responses are electronic letters to the editor. They enable our users to debate issues raised in articles published on Although a selection of rapid responses will be included online and in print as readers' letters, their first appearance online means that they are published articles. If you need the url (web address) of an individual response, perhaps for citation purposes, simply click on the response headline and copy the url from the browser window. Letters are indexed in PubMed.

Re: US healthcare company fires 69 employees for refusing flu vaccination Owen Dyer. 359:doi 10.1136/bmj.j5473

It seems to me that there is a Domino Theory haunting the minds of US vaccine authorities: they worry that if people refuse one vaccine, sooner or later they will refuse them all, and influenza vaccine is a special case. Left to their own devices, just 43% of US healthcare workers, including doctors and nurses, get the flu shot; this is a poor example for the general public, who may wonder why the other 57% don't get this "lifesaving vaccine." Thus, authorities ignore the evidence in setting what some of us consider a draconian policy mandating flu shots for healthcare workers. I remind you that we still do not know if seasonal flu shots actually do more good than harm in the long run, for healthcare workers or anyone else.

Competing interests: No competing interests

30 November 2017
Allan S. Cunningham
Retired pediatrician
Cooperstown NY 13326, USA
Re: Coffee consumption and health: umbrella review of meta-analyses of multiple health outcomes Peter C Hayes, Julie Parkes, et al. 359:doi 10.1136/bmj.j5024

Prof Koh has moved, thank goodness, to actually “quantifying” the amount of coffee it is safe to drink..unfortunately, he too is vague.

1. Three to five cups. What size cups are we talking about? Even Turkish coffee cups may hold three ounces or (the larger ones) six ounces.
A cappucino is traditionally, about six ounces.

2. Would you treat (to coffee) an 80 kg man exactly as you would a 60 kg man?

3. Do you see no difference between the consumption of Turkish coffee and, say, cappuccino? Or, Irish coffee?

Just because Christmas is round the corner, you cannot dismiss factors such as the chemical composition, the method of preparation, the amount consumed, the bulk of the consumer. Et cetera. Et cetera.

Umbrella Reviews are shot full of holes. Meta analyses of, say, Physalis edulis consumers are simpler - only two varieties. Meta analyses of the taste of baked apples are extremely difficult - Bramleys, Jonagolds, Lord Derbys are very different.

Competing interests: No competing interests

30 November 2017
JK Anand
Retired doctor
Free spirit
Re: Prevalence and clinical profile of microcephaly in South America pre-Zika, 2005-14: prevalence and case-control study Fernando A Poletta, Maria G Dutra, Flavia M Carvalho, Eduardo E Castilla, et al. 359:doi 10.1136/bmj.j5018


The work by Orioli et al analysing the ECLAMC (Latin American Collaborative Study of Congenital Malformations) surveillance data to establish a baseline prevalence of microcephaly in South America before the 2015-17 Zika virus pandemic is invaluable to our understanding of the impact of Zika virus on congenital disease[1].

As the authors point out, a major challenge to anyone trying to understand the association between Zika virus and microcephaly is a lack of an internationally agreed definition of microcephaly. ECLAMC uses an ICD-8 definition, but “…does not specify the growth chart to be used by hospitals...”. The lack of an international consensus is surprising and unfortunate. We urge the international medical community to reach a consensus definition of microcephaly in order to facilitate public health surveillance, research and eventually, interventions. We note that the World health Organisation has growth charts and these would be a good international standard[2], but other such charts exist[3, 4].

One of the interesting points highlighted by Orioli et al is that even before Zika virus arrived in South America, there was regional variation in microcephaly across that continent. For example, the authors found that Brazil already had a relatively high prevalence of microcephaly between 2005-14, as did the Metropolitana region of Chile.

In contrast, Uruguay had no cases of microcephaly detected and Paraguay had only one case of microcephaly detected in the study period, albeit with only 3 ECLAMC centres between them.

The reasons for the significant regional variations are not yet known. Clearly there is scope for more research to understand the reasons for these variations.

The authors reported possible risk factors like maternal age, congenital infections and consanguinity in detail. However, some factors which could be relevant were not reported in this study, including ethnicity, maternal pre-conception and early pregnancy alcohol use and maternal smoking.

Brazil has an ethnically diverse population, which includes people of African, European and Asian descent[5]. It would be interesting to see what if any effect ethnicity has on the prevalence of microcephaly in Brazil and South America.

Maternal alcohol use and smoking are both associated with reduced fetal head circumference and microcephaly[6,7]. Recently, there has been a suggestion that paternal pre-conception smoking may be associated with microcephaly[8]. These are possible confounding factors which future research could address.

The arrival of Zika virus in Brazil in 2015 and concern over an excess incidence of microcephaly shortly after led to the declaration of a public health emergency of international concern by WHO[9]. This study shows that before Zika virus arrived, Brazil already had a relatively high prevalence of microcephaly, higher than most other South American nations. Furthermore, it demonstrated significant but unexplained regional variations across the continent. Studies like this help our understanding of the effect of emergent teratogenic threats like Zika virus.

[1] Prevalence and clinical profile of microcephaly in South America pre-Zika 20015-14: prevalence and case-control study. Orioli IM et al. BMJ 2017;359:j5018 doi:
[2] World Health Organisation. Child growth standards – head circumference for age: (accessed 28/11/17)
[3] The INTERGROWTH-21st Network – Newborn size: (accessed 28/11/17)
[4] United States Centers for Disease Control, National Centre for Health Statistics, clinical growth charts:
[5] Central Intelligence Agency, The World Factbook: Brazil
[6] Relationship between head circumference, brain volume and cognition in children with prenatal alcohol exposure. Treit S et al. PLoS One 2016 (accessed 28/11/17)
[7] Prenatal tobacco exposure, biomarkers for tobacco in meconium and neonatal growth outcomes. Himes SK et al. J Pediatr. 2013 162(5):970-975 doi:
[8] Pre-conception and prenatal alcohol exposure from mothers and fathers drinking and head circumference: results from the Norwegian Mother Child Study (MoBa). Zuccolo L et al. SciRep 2016 doi:
[9] Zika virus is a global public health emergency, declares WHO. Gulland A. BMJ 2016;352:i657 doi:

Competing interests: The views expressed are our own and not those of our employers.

30 November 2017
Gee Yen Shin
Honorary Consultant Virologist
Mr Ramesan Navaratnarajah, Dr Rohini Manuel
London Northwest Healthcare NHS Trust
Department of Micobiology, Northwick Park Hospital, Watford Road, Harrow HA1 3UJ
Re: The responses to the “cancer drugs scandal” must fully involve patients—an essay by Tessa Richards Tessa Richards. 359:doi 10.1136/bmj.j4956

I have with great interest read the essay by Tessa Richards (1) on the response to the ”cancer drug scandal” and the many challenges faced by cancer patients during their treatment trajectory. Inadequate discussions about treatment efficacy, toxicity and potential late side effects seem universal across countries. Waiting times, national guidelines and fast-track cancer pathways are also governing the way the healthcare system is organized and run in Denmark and the importance of responding to individual patients’ needs and preferences is easily forgotten.

New cancer drugs are often approved based on a marginal survival benefit over existing treatment options or placebo. In this context, it is worth noting that the approvals are often based on a significant statistical difference in favour of the new drug, but it is rarely emphasized whether this difference is of substantial clinical benefit for the patients That may be one of the reasons why many clinicians don't take the medicines they prescribe for their patients (2). Given the difference between statistical difference and clear clinical benefit it's vital that healthcare professionals discuss the magnitude (or lack of) of potential benefit, harms and risks with patients and relatives. Studies suggest that oncologists often do not involve patients in the decision-making process to the extent they desire (3-5). In addition, little attention has been paid to organisational and system level factors in which these interactions and decisions are embedded, and how to modify these to ensure that shared decision making becomes part of routine practice.

Our response to this challenge has been to establish a Centre for Shared Decision Making (6-7) at a Danish cancer hospital and to propose a systematic, organisation-wide collaborative team based approach aimed at embedding a patient centred culture from top to bottom in clinical cancer care. The Centre for Shared Decision Making has three patients on board in the steering committee. Our experience has shown the need for solid management foundation and support, early involvement of leading clinicians in building patient decision aids, and training staff in how to do shared decision making in practice. We have developed five decision aids for breast cancer, lung cancer, herniated disc genetic testing and CA125 follow-up for ovarian cancer patients to be used during consultation with the clinician. The patient decision aids have been developed in co-creation with patients and with a school of design. The clinical testing of our decision aids is entering the last project phase and we have started up a project, to support clinicians to build their own decision aids for conditions and situations which are of particular interest to them and their patients. To facilitate this we have developed a manual to inform the compilation of an online decision aid template with specific content. Currently, we then use print version of the patient decision aids in the clinic since this is the format we have found that our cancer patients, who are usually older, prefer.

A major challenge has been to combat the “I do it already culture” – from clinicians who believe that shared decision making is old wines in new bottles and that they do it already. Furthermore, there are many misconceptions. For example clinicians may use the phrase "It's you as a patient who decides" and thereby think they are practicing shared decision making without understanding that it’s the process which should be shared and that one of their most important tasks is to help the patient to identify what is important to him or her and to facilitate decision making together.

In my opinion, we need to confront our failure to understand and implement shared decision making if we are to get better at it and to ensure that both health professionals and the public fully understand what the process entails.


1: Richards T: The responses to the “cancer drugs scandal” must fully involve patients—an essay by Tessa Richards. BMJ 2017;359:j4956

2: Periyakoil VS, Neri E, Fong A, Kraemer H (2014) Do Unto Others: Doctors' Personal End-of-Life Resuscitation Preferences and Their Attitudes toward Advance Directives. PLoS ONE 9(5): e98246.

3. Tariman JD, Berry DL, Cochrane B, Doorenbos A, Schepp K. Preferred and actual participation roles during health care decision making in persons with cancer: a systematic review. Annals of oncology : official journal of the European Society for Medical Oncology / ESMO. 2010;21(6):1145-51.

4. Kehl KL, Landrum MB, Arora NK, Ganz PA, van Ryn M, Mack JW, et al. Association of Actual and Preferred Decision Roles With Patient-Reported Quality of Care: Shared Decision Making in Cancer Care. JAMA Oncol. 2015;1(1):50-8.

5. Stacey D, Samant R, Bennett C. Decision making in oncology: a review of patient decision aids to support patient participation. CA Cancer J Clin. 2008;58(5):293-304.

6. Centre for Shared Decision Making – Lillebaelt Hospital. Webpage:

7. Dahl Steffensen K, Hjelholt Baker V, Vinter MM. Implementing shared decision making in Denmark: First steps and future focus areas. Z Evid Fortbild Qual Gesundhwes. 2017 Jun;123-124:36-40.

Karina Dahl Steffensen
MD, PhD, Associate Professor
Director Center for Shared Decision Making
Department of Clinical Oncology
Vejle Hospital
Beriderbakken 4
DK-7100 Vejle

Institute of Regional Health Research
Faculty of Health Sciences
University of Southern Denmark
Winsl?wparken 19, 3
DK-5000 Odense C

Competing interests: No competing interests

30 November 2017
Karina Dahl Steffensen
Director Center for Shared Decision Making
Department of Clinical Oncology, Vejle Hospital, Beriderbakken 4, DK-7100 Vejle, Denmark
Re: Agreement of treatment effects for mortality from routinely collected data and subsequent randomized trials: meta-epidemiological survey Lars G Hemkens, Despina G Contopoulos-Ioannidis, John P A Ioannidis. 352:doi 10.1136/bmj.i493

I would like to add some mathematical considerations regarding the simulations provided in the recent response by Franklin et al.

The method by Hemkens et al. is based on an inversion rule, i.e. setting the sign of log(OR_RCD) to be negative. This means that log(OR_RCD) cannot be assumed to follow a normal distribution, as it is truncated to negative values. In fact, if the log(OR_RCD) before inversion follows a normal, after the inversion it follows a folded normal distribution. The distribution of log(OR_RCT) on the other hand remains unaffected.

In other words, if we start with a distribution of log(OR_RCD) centered at a specific value, after applying the inversion rule the mean will always shift to a smaller value, while the mean of log(OR_RCT) will not change. Moreover, the log(ROR), which is defined as the difference between the two, cannot be assumed to follow a normal distribution after implementing the inversion rule.

The bias that was shown in the simulations by Franklin et al. can be calculated analytically. E.g. for the first scenario, where the mean is 0 and standard deviation is sigma=0.5, the mean of the folded normal distribution (that logOR_RCD follows after the rule) is sigma*sqrt(2/Pi)=0.40. On the other hand, log(OR_RCT) still follows the normal, and the mean is still 0. The overall bias in the log(ROR) scale is the difference between the two, i.e. 0.40, which translates in a ROR=exp(0.40)=1.49, exactly as found in the simulations.

Finally, note that when treatment effects are very large and/or the standard deviations are sufficiently small, the normal and the corresponding folded normal distributions are almost identical. In such cases, the bias introduced in ROR by using this inversion rule will be negligible.

Orestis Efthimiou

Competing interests: No competing interests

30 November 2017
Orestis Efthimiou
Post-doctoral researcher
University of Bern
Institute of Social and Preventive Medicine
Re: Coffee consumption and health: umbrella review of meta-analyses of multiple health outcomes Peter C Hayes, Julie Parkes, et al. 359:doi 10.1136/bmj.j5024

Poole R, et al1 evaluated the existing evidence for associations between coffee consumption and multiple health outcomes by performing an umbrella review of the evidence across meta-analyses of observational and interventional studies. The umbrella review identified 201 meta-analyses of observational research with 67 unique health outcomes and 17 meta-analyses of interventional research with nine unique outcomes. Coffee consumption was more often associated with benefit than harm for a range of health outcomes across exposures including high versus low, any versus none, and one extra cup a day. There was evidence of a non-linear association between consumption and some outcomes, with summary estimates indicating largest relative risk reduction at intakes of three to four cups a day versus none, including all cause mortality, cardiovascular mortality, and cardiovascular disease. High versus low consumption was associated with an 18% lower risk of incident cancer. Coffee drinking seems safe within usual patterns of consumption, except during pregnancy and in women at increased risk of fracture.

Korea is the second most frequently consumed country worldwide. Many Korean patients with cardiovascular diseases would like to enjoy drinking coffee, however most do not because of incorrect knowledge. Previous studies already reported similar results. Indeed, Harvard group tried to elucidate considerable controversy on the association between coffee consumption and cardiovascular disease (CVD) risk by performing a meta-analysis with thirty-six studies including 1 279 804 participants and 36 352 CVD cases. A nonlinear relationship of coffee consumption with CVD risk was identified. The study demonstrated that a nonlinear association between coffee consumption and CVD risk was observed and moderate coffee consumption was inversely significantly associated with CVD risk, with the lowest CVD risk at 3 to 5 cups per day, and heavy coffee consumption was not associated with elevated CVD risk.2

This group also investigated an inverse association of caffeinated and decaffeinated coffee consumption and risk of type 2 diabetes by performing a systematic review and a dose-response meta-analysis. They observed that coffee consumption was inversely associated with the risk of type 2 diabetes in a dose-response manner. Both caffeinated and decaffeinated coffee was associated with reduced diabetes risk.3

In summary, moderate coffee intake can be part of a healthy diet.4 However, should doctors recommend drinking coffee to prevent disease? Should people start drinking coffee for health reasons? Guallar answered “no” to both questions in his editorial.5 The evidence is so robust and consistent across studies and health outcomes, however, that we can be reassured that drinking coffee is generally safe, although some caveats apply. the amount consumed is important. For many endpoints, the lowest risk of disease is associated with drinking three to five cups of coffee a day. Higher intake may reduce or reverse the potential benefit, and there is substantial uncertainty, both in individual studies and in meta-analyses, about the effects of higher levels of intake. The safety of coffee should thus be restricted to moderate intake, generally considered as ≤400 mg of caffeine a day (about four or five coffee drinks).5

Funding: None, Disclosures: None

1. Poole R, Kennedy OJ, Roderick P, Fallowfield JA, Hayes PC, Parkes J. Coffee consumption and health: umbrella review of meta-analyses of multiple health outcomes.
BMJ. 2017;359:j5024.

2. Ding M, Bhupathiraju SN, Satija A, van Dam RM, Hu FB. Long-term coffee consumption and risk of cardiovascular disease: a systematic review and a dose-response meta-analysis of prospective cohort studies. Circulation. 2014;129:643-659.

3. Ding M, Bhupathiraju SN, Chen M, van Dam RM, Hu FB. Caffeinated and decaffeinated coffee consumption and risk of type 2 diabetes: a systematic review and a dose-response meta-analysis. Diabetes Care. 2014;37:569-86.

4. Guallar E, Blasco-Colmenares E, Arking DE, Zhao D. Moderate coffee intake can be part of a healthy diet. Ann Intern Med. 2017;167:283-284.

5. Guallar E. Coffee gets a clean bill of health. BMJ. 2017;359:j5356.

Competing interests: No competing interests

30 November 2017
Professor of Medicine
Department of Cardiovascular Medicine, Gachon University, Gil Medical Center
774 Beongil 21, Namdongdaero, Namdong-Gu
Re: Medicines, excipients, and dietary intolerances Drug and Therapeutics Bulletin. 358:doi 10.1136/bmj.j3468

Food-drug or herb-drug or drug-drug interactions are common in those who have dietary intolerance or allergies. This may be Pharmacokinetic or pharmacodynamic in nature. It is observed that the prevalence of concomitant administration excipients in conventional medications will increase the probability of Dietary intolerance or Allergies. To date, there is no standard system for evaluating the excipients and their role in drug interaction in medicines. So this is the right time to develop patient-specific and drug-specific variables that can affect Pharmacokinetic or pharmacodynamics of any dosage modalities especially oral dosage forms.

Absorption of all oral dosage modalities is under the influence of range of factors such as bioavailability, passive transporters, active transporters and drug absorption. Drug transporters have a role in drug development like distribution of drug distribution and facilitation of the transport across the cell (1), etc. The discipline of Drug transporters deals with transport biology and has grown remarkably over the last decade. This specialized field plays a significant role in disease manifestation and its management. Drug transporters help in recognizing those factors that may modify the time and rate of drug absorption. There are many types of Drug transporters now existing in practice. For instance, Membrane transporters directly affecting drug absorption (2). P-glycoprotein is a membrane transporter (drug transporter) that plays a vital role in drug absorption and bioavailability. Medicine excipients in dosage modalities are a special class of P-glycoprotein inhibitors (3). It is observed that P-glycoprotein inhibiting excipients reported are non-absorbable, and they will inhibit P-glycoprotein only in the gastrointestinal tract. So this is the right time to focus more on drug transporters to overcome drug intolerance and allergies.


1. Hediger M.A., et al. The ABCs of membrane transporters in health and disease (SLC series): Introduction.Molecular Aspects of Medicine 2013; 34: 95–107
2. Martinez, M.N., Amidon, G.L., A Mechanistic Approach to Understanding the Factors Affecting Drug Absorption: A Review of Fundamentals. The Journal of Clinical Pharmacology, 2002; 42: 620–643.
3.Varma M, V, Ashokraj Y, Dey C.S, Panchagnula.R., P-glycoprotein inhibitors and their screening: a perspective from bioavailability enhancement Pharmacol. Res.2003; 48: 347-359.

Competing interests: No competing interests

30 November 2017
Kamath Madhusudhana
Associate Professor
Division of Ayurveda ,Center for Integrative Medicine and Research ( CIMR ) Manipal University ,Manipal.
Re: Support mounts for paediatrician whom GMC wants struck off Deborah Cohen. 359:doi 10.1136/bmj.j5384

Dear Editors

I share similar concerns with other respondents in which a doctor is held responsible for a death of a child in 2011 resulting from multiple failures of a system under pressure; I am surprised the 2015 conviction had not been followed by any successful appeal.

My attempts at finding 2013 coroner's proceedings and 2015 court proceedings have been unsuccessful; I would appreciate it if fellow readers can offer suggestions and point me in the right direction.

However in response to Dr Ashworth's post (ref 1), I like to also share my concerns about the nature of how the legal proceedings have occurred and the resulting public perception of the case.

These concerns are precipitated by inadvertent discovery of a webpage (Ref 2) which described the FOI request on 5 November 2015 of a person named 'Chris' seeking to find out about Bawa-Garba's medical qualification.

"Where did Dr Hadiza Bawa-Garba (Leicester Royal Infirmary) qualify and what medical qualifications does she hold"

The result of the FOI provided on 24 Nov 2015 (prior to conviction of Bawa-Garba in Dec 2015) clearly shows that Dr Bawa-Garba is a graduate of University of Leicester in 2003.

Without over-speculating "Chris" original purpose seeking the FOI, or the intent on how this information was going to be used, I simply wonder what would have occurred if Bawa-Garba was instead a foreign graduate?

Furthermore I have found that the webpage displaying the FOI is invariably found within the top 20 Google listing when searching with keywords involving "Bawa-Garba" and "GMC".

I have personal concerns from this discovery about the nature of public perception of Dr Bawa-Garba and whether it would have any effect on the court proceedings and subsequent management of processed by the GMC as a result.

Would this revelation reflect on the prejudices of certain parts of the wider community, I wonder.


Competing interests: No competing interests

29 November 2017
Shyan Goh
Orthopaedic Surgeon
Sydney, Australia
Re: Non-antibiotic options for recurrent urinary tract infections in women Jonathan Barclay, Rajan Veeratterapillay, Chris Harding. 359:doi 10.1136/bmj.j5193

May I thank Prof Alraek for mentioning the possible value of acupunture in this condition?
I sincerely hope that the British medical establishment will not suffer an apoplectic fit.
The mechanism postulated by Prof Alraek seems reasonable.

Competing interests: No competing interests

29 November 2017
JK Anand
Retired doctor
Free spirit
Re: Agreement of treatment effects for mortality from routinely collected data and subsequent randomized trials: meta-epidemiological survey Lars G Hemkens, Despina G Contopoulos-Ioannidis, John P A Ioannidis. 352:doi 10.1136/bmj.i493

We are grateful to Franklin et al. for highlighting the additional graphs. They are very helpful for trying to bring some closure to our discussion. Contrary to their assertion, what their simulations show is the classic, well-known principle of regression-to-the-mean: when measurements are sampled from an empirical distribution using some selection rule that selects only for one side or one tail of the distribution (or in any way that the mean of the selected sample is less than the true mean), then a repeat (e.g. re-measurement or subsequent replication study) will regress towards the mean. One of us has written repeatedly on how this principle contributes (along with several other factors) towards inflating the results of early studies in diverse fields of research. For a representative overview, please see (1).

Take the null true effect simulation, for example. The inversion makes all RCD estimates to have logOR<0, while the true logOR is 0. In the 9 non-inversed cases (where the selection rule is: logOR<0 in the RCD studies), the subsequent RCTs will regress towards the mean, to the truth of logOR=0 in this case (and of course some RCTs may “overshoot” even to values logOR>0 in the process, due to random error), i.e. the logOR of the RCTs is expected, on average, to be larger. Similarly, in the 7 inverted cases (where the selection rule is: before inversion logOR>0 in the RCD studies), again the subsequent RCTs will regress to the truth of logOR=0; again, due to the inversion the logOR of the inverted RCTs is expected, on average, to be larger than the logOR of the inverted RCD studies. Thus consistently an ROR>1 is generated.
Similarly, also on the other simulated cases, the subsequent RCTs regress towards the mean, towards the assumed true value (which is non-null in these cases). Thus, the ROR will similarly be above 1.

The pattern that Franklin et al. describe so nicely in these graphs is exactly one of the reasons (besides many other reasons, as we explained before) why in the clinical scenario where only RCD results are available and they show that one of two compared treatments is the best, this evidence should be seen with great caution. Clinicians may often get it way wrong if they chose the treatment that seems to be the best based on these RCD studies.

Interestingly, this pattern (regression-to-the-mean as we call it or bias as Franklin et al. non-specifically call it), is not that prominent in our data compared with what is shown in the simulated graphs by Franklin et al. Even without any inversion, we estimate a summary ROR of 1.25, not much different from 1.31. This may be because only 3 of the 16 comparisons had to be inverted so as to match the meaningful clinical question in our real data, while 7 to 13 of the 16 comparisons were inverted in the Franklin et al. graphed simulations. In situations where this pattern (regression-to-the-mean, or whatever one wants to call it) might be more prominent (e.g. as in the Franklin et al. graphs), caution about using the early RCD evidence would be even greater, not less.

Lars G. Hemkens
Despina G. Contopoulos-Ioannidis
John P.A. Ioannidis

1. Ioannidis JP. Why most discovered true associations are inflated. Epidemiology. 2008 Sep;19(5):640-8.

Competing interests: No competing interests

29 November 2017
Lars G. Hemkens
Senior Scientist
Despina G. Contopoulos-Ioannidis, John P.A. Ioannidis
University Hospital Basel
Spitalstrasse 12, CH-4031 Basel


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