Confronting the Frustrations of Negative Clinical Trials in Acute Respiratory Distress Syndrome
Bibliographic record
Abstract
Despite robust successes in trials of mechanical ventilation, pharmacologic interventions in acute respiratory distress syndrome have been disappointing. Although ineffective therapy remains the compelling explanation for these negative trials, other possible explanations exist. These negative trials, better termed "statistically negative trials" or "indeterminate trials," cannot prove that a therapy is ineffective. It is important for clinicians and investigators to appreciate the alternative explanations for negative trials of potentially effective therapies because these indicate options for improving clinical trials in acute respiratory distress syndrome. These options can be organized into strategies that increase sample size, increase the signal from the therapy, and reduce the noise or variation in the study. Each of the strategies to improve the likelihood of a positive clinical trial poses a potential tradeoff in generalizability, cost, sample size, signal, or noise.
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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 | 0.038 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".