Bibliographic record
Abstract
An ongoing challenge in clinical research is the inaccessibility of clinical trial data, which prevents physicians from making an informed decision with regards to patient care. The U.S. Food and Drug Administration (FDA) as well as the World Health Organization (WHO) recently called for all trial data to be registered and made publically available. However, this issue is still ongoing and there are several measures currently being enforced to rectify these concerns. Potential solutions, such as regulations, campaigns, and possible conse- quences, for increasing transparency in clinical trial data will be discussed. RÉSUMÉ L’inaccessibilité des données provenant d’essais cliniques constitue un défi constant en recherche clinique, puisqu’elle empêche les médecins de prendre des décisions éclairées quant aux soins de leurs patients. Récemment, le Secrétariat américain aux produits alimentaires et pharmaceutiques (FDA) ainsi que l’Organisation mondiale de la Santé (OMS) ont demandé que toutes les données d’essais cliniques soient enregistrées et mises à la disposition du public. Toutefois, ce problème persiste et plusieurs mesures ont été mises en place pour répondre à ces préoccupations. Des solutions possibles dont des réglementations, des campagnes et des sanctions possibles pour améliorer la transparence en ce qui concerne les données d’essais cliniques seront discutées.
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 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 | 0.541 | 0.718 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.006 | 0.040 |
| Scholarly communication | 0.034 | 0.040 |
| Open science | 0.014 | 0.018 |
| Research integrity | 0.024 | 0.043 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".