Clinical research ethics for critically ill patients: A pandemic proposal
Bibliographic record
Abstract
Pandemic H1N1 influenza is projected to be unprecedented in its scope, causing acute critical illness among thousands of young otherwise healthy adults, who will need advanced life support. Rigorous, relevant, timely, and ethical clinical and health services research is crucial to improve their care and outcomes. Studies designed and conducted during a pandemic should be held to the same high methodologic and implementation standards as during other times. However, unique challenges arise with the need to conduct investigations as efficiently as possible, focused on the optimal outcome for the individual patient, while balancing the need for maximal societal benefit. We believe that clinical critical care research during a pandemic must be approached differently from research undertaken under nonemergent circumstances. We propose recommendations to clinical investigators and research ethics committees regarding clinical and health services research on pandemic-related critical illness. We also propose strategies such as expedited and centralized research ethics committee reviews and alternate consent models.
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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 | 0.267 | 0.253 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.042 |
| Scholarly communication | 0.018 | 0.028 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.080 | 0.063 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".