Registering transparency: the making of the international clinical trial registry platform by the world health organization (2004–2006)
Bibliographic record
Abstract
BACKGROUND: This paper examines the events and conditions that led to the creation of the International Clinical Trials Registry Platform (ICTRP) in 2006 by the World Health Organization (WHO), and how the WHO addressed the issue of transparency in global pharmaceutical research. Using historical textual analysis, I trace the scientific debates that advocated for the establishment of official clinical trial registries in medical journals, and the sequence of actions following the GSK Paxil scandal in 2004, identifying the major ethical and scientific arguments that led to the involvement of the WHO as a key actor in trial registration in the context of the Big Pharma business model. RESULTS: Through the questions "Why register?" and "Why registries?" as a roadmap, I examine the issues of publication bias and selective reporting by the industry, scrutinizing two ways in which the practice of publication bias damaged transparency in industry-sponsored research. The first involved ethical concerns regarding human subject exploitation and concealing of negative results. The second addresses the deterioration of the certainty of evidence due to incomplete access to trials results. By reviewing the series of events that occurred between 2004 and 2006 -between the Paxil scandal and the launch of the ICTRP-, I analyze the actions taken by the different actors involved: (1) the International Committee of Medical Journal Editors (ICMJE) and the creation of the Ottawa Group; (2) the WHO, beginning with the Ministerial Summit on Health Research held in November of 2004, and (3) the responses of the pharmaceutical industry and specifically GSK to the call for transparency and trial registration. CONCLUSIONS: The history of trial registration through the ICTRP as a dataveillance apparatus shows the difficulty of regulating a health enterprise turned into a global business. Moreover, it shows the challenges of globalization and how easier and faster it is to globalize business compared to good practices, raising the question of why it has been so hard to undo these trends. Indeed, the history of the movement for trial registration is not a history of regulation success, or at least not yet.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".