Clinical Trial Transparency and Orphan Drug Development: Recent Trends in Data Sharing by the Pharmaceutical Industry
Bibliographic record
Abstract
BACKGROUND: Data sharing from clinical trials can be key to the development and approval of medicines for rare diseases. Many events during the first half of 2013 have contributed to the movement for increased transparency. These include the development of the European Medicines Agency's new data publication policy, the creation of the AllTrials petition and GlaxoSmithKline's choice to sign it, the launch of GlaxoSmithKline's system for access to patient-level clinical trial data and Roche's commitment to create a similar system, the release of results from the Yale University Open Data Access project's first medicine analysis for Medtronic, and the creation of the Reg4All website. AIMS/OBJECTIVES: This paper summarises major developments in clinical trial transparency between January and June 2013 and analyses the composition of datasets released by GlaxoSmithKline. METHODS: GlaxoSmithKline's database of available trials was tabulated and graphs of relevant trial characteristics were produced. RESULTS/CONCLUSIONS: Due to current transparency initiatives, it is likely that much more data will be made available over the next few years through systems similar to GlaxoSmithKline's. Although some aspects of GlaxoSmithKline's model could limit its usefulness, the data currently listed is diverse and could be promising for researchers interested in rare disease treatment.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".