Doit-on inclure des données non publiées (abrégés de congrès) dans les évaluations des comités de pharmacologie ?
Bibliographic record
Abstract
Resume : Objectif : Presenter une revue de la litterature medicale sur l’utilisation des donnees probantes non publiees et la methodologie utilisee par le Programme de gestion therapeutique des medicaments pour la prise en compte de ces donnees lors de la preparation d’evaluations destinees aux comites de pharmacologie. Mise en contexte : Le fait que les etudes positives soient plus souvent publiees que les etudes negatives et le long delai entre la presentation des resultats lors de congres et leur publication subsequente sont des arguments en faveur de l’utilisation des donnees non publiees. Par contre, l’information limitee disponible dans l’abrege et le fait que de nombreuses analyses ont montre des differences importantes entre l’abrege et la publication subsequente demontrent les limites associees a l’utilisation de donnees non publiees. L’analyse des abreges et des publications pour le trastuzumab (cancer sein adjuvant) demontre une concordance parfaite entre le resume et l’article publie, ce qui n’est pas le cas pour le bortezomib (myelome multiple), pour lequel on note des differences importantes a propos de l’innocuite. Conclusion : Les comites de pharmacologie devraient tenir compte du statut de publication lors de la prise de decision et devraient analyser avec rigueur et prudence une demande supportee uniquement par des donnees non publiees. Abstract Objective : To present a literature review on the use of non-published data and the methods used by the Programme de gestion therapeutique des medicaments in the consideration of data to be presented to pharmacy and therapeutics committees. Context : The fact that studies with positive outcomes are more often published than those with negative outcomes and the fact that there is a long delay between the presentation of results at conferences and their subsequent publication are two arguments in favor of using unpublished data. The limited information available from the abstract, however, and the fact that numerous analyses have shown important differences between the abstract and the subsequent publication demonstrate the limits associated with using non-published data. The analysis of abstracts and publications for trastuzumab (adjuvant treatment of breast cancer) has shown perfect concordance between the abstract and published article, a concordance that is not the case for bortezomib (multiple myeloma), where important differences were observed with respect to safety information. Conclusion : Pharmacy and therapeutics committees should consider only published articles and should rigorously and prudently analyze any request supported only by non-published data. Key words : abstract; publication; bias.
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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 | 0.463 | 0.749 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 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".