Maximizing value and minimizing waste in clinical trials in swine: Selecting outcomes to build an evidence base
Bibliographic record
Abstract
Researchers planning clinical trials should identify the primary trial outcome and adequately power the trial to detect clinically meaningful differences in this outcome. All primary and secondary outcomes and their measurement should be comprehensively described, and their results reported. There is evidence that trials on the same subject use different outcomes or measure the same outcome in different ways, making it difficult to compare intervention effectiveness across clinical trials. Consensus development of core outcome sets could improve consistency in outcome measures used across trials and aid in development of an evidence-based body of literature on intervention effectiveness in swine populations.
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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 | 0.796 | 0.842 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.007 |
| Bibliometrics | 0.026 | 0.016 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".