Can J Respir Ther Vol 49 No 4 Winter 2013-201420 Clinical trials registration
Bibliographic record
Abstract
Over the past eight years, the registration of clinical trials has been strongly advocated by journals and editors, and is now considered to be mandatory for publication by most medical journals. In the United States, it became legally mandated by the Food and Drug Administration in 2007 (1), although Canada has no law requiring registration. Registration of a clinical trial ensures that the public has access to information regarding trials involving human subjects and health out-comes. Published clinical trials strongly affect decision making in health care, including decisions made at the bedside, in the boardroom and in the legislature. Therefore, the public needs to have access to the same evidence as the decision makers. Moreover, registering clin-ical trials and their protocols before data collection begins helps cor-rect the distortion created by selective reporting in the literature (ie, ‘positive publication bias’), in which only trials with positive outcomes are published. Many also believe that the research community, by using human subjects, has a moral obligation to the public to publish their findings and a registry can reveal a disconnect in communica-tion. In addition, registries can help standardize and improve clinical trial protocols, reduce overlapping or redundant publication and scien-tific misconduct, and improve accuracy in reporting. Several registries exist today, with the largest at
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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 | 0.070 | 0.279 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.218 | 0.074 |
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".