Extended Versus Standard Proning Duration for COVID-19–Associated Acute Respiratory Distress Syndrome: A Target Trial Emulation Study
Bibliographic record
Abstract
Abstract Rationale Prone positioning for ⩾16 hours in moderate-to-severe acute respiratory distress syndrome (ARDS) improves survival. However, the optimal duration of proning is unknown. Objectives To estimate the effect of extended versus standard proning duration on patients with moderate-to-severe coronavirus disease (COVID-19) ARDS. Methods Data were extracted from a five-hospital electronic medical record registry. Patients who were proned within 72 hours of mechanical ventilation were categorized as receiving extended (⩾24 h) versus standard (16–24 h) proning based on the first proning session length. We used a target trial emulation design to estimate the effect of extended versus standard proning on the primary outcome of 90-day mortality and secondary outcomes of ventilator liberation and intensive care unit (ICU) discharge. Analytically, we used inverse probability of treatment weighted (IPTW) Cox or Fine-Gray regression models. Results A total of 314 patients were included; 234 received extended proning, and 80 received standard-duration proning. Patients who received extended proning were older, had greater comorbidity, were more often at an academic hospital, and had shorter time from admission to mechanical ventilation. After IPTW, characteristics were well balanced. Unadjusted 90-day mortality in the extended versus standard proning groups was 39% versus 58%. In doubly robust IPTW analyses, we found no significant effects of extended versus standard proning duration on mortality (hazard ratio [95% confidence interval], 0.95 [0.51–1.77]), ventilator liberation (subdistribution hazard, 1.60 [0.97–2.64], or ICU discharge (subdistribution hazard, 1.31 [0.82–2.10]). Conclusions Using target trial emulation, we found no significant effect of extended versus standard proning duration on mortality, ventilator liberation, or ICU discharge. However, given the imprecision of estimates, further study is justified.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".