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Record W7135887868

Bumps on the Road Towards Clinical Trials Data Transparency- A recent U-Turn by the EMA?

2014· other· en· W7135887868 on OpenAlexaboutno aff
Timo Minssen

Bibliographic record

VenueResearch at the University of Copenhagen (University of Copenhagen) · 2014
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsConfidentialityTransparency (behavior)Clinical trialParliamentLegislatureData Protection Act 1998Public consultationPublic accessData access
DOInot available

Abstract

fetched live from OpenAlex

In a recent blog I discussed the benefits and potential draw-backs of a new “EU Regulation on clinical trials on medicinal products for human use”, which had been adopted by the European Parliament and Council in April 2014. Parallel to these legislative developments, the drug industry has responded with its own initiatives providing for varying degrees of transparency. But also medical authorities have been very active in developing their transparency policies. In the US, the FDA proposed new rules which would require disclosure of masked and de-identified patient-level data. In the EU, the EMA organized during 2013 a series of meetings with its five advisory committees to devise a draft policy for proactive publication of and access to clinical-trial data. In June 2013 this process resulted in the publication, of a draft policy document titled “Publication and access to clinical-trial data” (EMA/240810/2013). Following an invitation for public comments on this document, the EMA received more than 1,000 submissions from stakeholders. Based on these comments the EMA recently proposed “Terms of Use” (TOU) and “Redaction Principles” for clinical trial data disclosure. In a letter to the EMA’s executive director Dr. Guido Rasi, dated 13 May 2014, the European Ombudsman, Emily O'Reilly, has now expressed concern about what seems to be a substantial shift of policy regarding clinical trial data transparency. According to the proposed “Terms of Use” (TOU) and “Redaction Principles” (RPs) it seems that EMA plans to make available clinical-trial data, with the exception of what it considers to constitute 'Commercial confidential Information'. This data is only disclosed to natural or legal persons or organisations that have registered with EMA and that agree to the conditions for such access that are set down in the 'Terms of use'. The TOU and RPs impose wide restrictions on the use of such data, which are only displayed on a screen using an interface provided by EMA. In light of these developments, the Ombudsman had asked EMA's Executive Director, Guido Rasi, to explain how the EMA plans to address requests for public access to existing clinical trial data. She also requested an explanation of the motives and the legal basis for what seems to be a significant change of policy. In his response-letter dated May 22nd , Dr. Rasi rejected O’Reilly’s concerns and replied that EMA’s new policy is a ‘reasonable compromise’ that does not prevent researchers from asking for access to specific data sets on the basis of the existing access to information policy. This response has been heavily criticized on a recent PLoS Blog by Trudo Lemmens of the University of Toronto, who also had sent a letter to EMA’s executive director outlining several legal concerns. Professor Lemmens writes i.a. that: ¨"In short, EMA’s approach is strengthening industry’s legal control over data, making it more difficult and legally risky for independent scientists to use them. These are in essence regulatory data, created for public interest use. For the EMA, a key public institution, to now support the privatizing of pharmaceutical knowledge through contractual affirmations of companies’ rights over these data is truly astounding." These critical remarks also find support in a rapid response by German experts in the British Medical Journal, who warn that the proposed EMA policy presents an obstacle to meaningful scientific use and contradicts in many respects both the Ombudsman draft recommendations on clinical trials transparency and the transparency related provisions in the new EU Clinical Trials Regulation. These reactions raise indeed serious issues in light of the recent shift towards more transparency. Yet, the evolving resistance against full data disclosure does not come as a complete surprise. The industry, authorities and policy makers might have acknowledged that the enormous benefits of more clinical trial data transparency outbalances potential downsides. However, and as discussed in my previous blog, the potential costs and concerns are also significant, in particular with regard to worries about patient privacy and insufficient intellectual property protection. While increased transparency will i.a. provide a better environment for identifying new avenues of promising research, too much transparency might actually decrease incentives for drug companies to invest in R&D and clinical trials. Since an effective development of cutting edge drugs and innovative medical uses requires both private and public involvement, these issues should be considered very carefully. The discussions are complicated by the risk that the industry might very well turn to alternative strategies in order to limit transparency and to protect their “know how” related to clinical trials. New battle fields might open up in the course of secret Free Trade Agreements (FTAs) on regulatory data exclusivities, and in the context of the ongoing EU efforts to harmonize and strengthen the protection of trade secrets. All of this indicates that the debates on clinical trials transparency are far from over. Much needs to be done to achieve a reasonable and utilitarian comprise promoting both medical innovation and access to medicine. As mentioned before, we are engaged in further research on these issues and we will keep you updated.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.318
metaresearch head score (Gemma)0.469
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.682
Threshold uncertainty score0.842

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3180.469
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0040.007
Science and technology studies0.0110.033
Scholarly communication0.0460.062
Open science0.0080.019
Research integrity0.0730.107
Insufficient payload (model declined to judge)0.0200.010

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.438
GPT teacher head0.445
Teacher spread0.007 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainReproducibility
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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