Why industry should register and disclose results of clinical studies—perspective of a recovering academic
Bibliographic record
Abstract
Why industry should register and disclose results of clinical studies-perspective of a recovering academicJesse A Berlin Although it's not typical, in a scholarly publication such as the BMJ, to add a personal perspective to a commentary, I believe that in this situation my background is relevant to the discussion.My doctoral dissertation, written in 1988, dealt with the topic of publication bias, which was well described in the Ottawa statement appearing in this issue.1 Since then, I've contributed to several studies of factors affecting publication, including an early empirical demonstration of publication bias. 2 About six months ago, I moved from a university, where I had spent 15 years, to a position in a large pharmaceutical research and development group.Registration and disclosure of the results of clinical studies have, not surprisingly, been topics of numerous conversations where I work.At this point, as suggested in the Ottawa statement, there is no longer any doubt that studies, whether sponsored by pharmaceutical companies or otherwise, will be registered and their results will be disclosed.This must be viewed as an overall positive step.The debate has changed focus from whether the research process will become more transparent, to how to carry out registration and disclosure.In the long run, openness of the research process will be good for patients, their families, their caregivers, and ultimately for business.The key area of disagreement between industry positions 3 4 and other proposals relates to the scope of registries.Specifically, the issue is whether all early phase, exploratory studies, or those not testing hypotheses, require registration and disclosure.As noted in the Ottawa statement, the industry position is that results from exploratory studies may be provided if the results are regarded as "medically important," and the results could change the labelling of products.Furthermore, results from failed investigational compounds may also be provided if medically important.Clearly, the definition of "medically important" is both crucial to this discussion and subjective in nature.A key component of the interpretation at Johnson & Johnson Pharmaceutical Research and Development is whether the results indicate harm or lack of efficacy, in which case we will disclose results.We would not rely on results of uncontrolled studies in support of efficacy, but when there is suggestion of harm, we have an obligation at our company to report that information.We believe this approach is consistent with the spirit of the Ottawa statement.What about the question of ethical obligations to research participants?Clearly, we owe our research participants a huge debt.Does that debt extend to full public disclosure of small, uncontrolled studies?By no means am I arguing against disclosure.However, as scientists, we are all aware of the difficulty in interpreting results of small, uncontrolled studies.Even with larger, well controlled studies, there is potential for individual study results to conflict with each other or Gregory J. Lectures on the duties and qualifications of a physician.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.052 | 0.264 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.017 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.107 | 0.106 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".