Awake Proning Reduces the Likelihood of Intubation and Mortality in Adult COVID-19 Patients With Acute Hypoxemic Respiratory Failure: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Purpose: To estimate the extent to which awake prone positioning (APP), in comparison to usual care, reduces the incidence of intubation and mortality in non-intubated patients with acute hypoxemic respiratory failure (AHRF) secondary to COVID-19. Method: PubMed, MEDLINE, EMBASE, CINAHL, Web of Science, Physiotherapy Evidence Database, and Scopus were searched from January 1, 2020, to August 2, 2023, for randomized controlled trials (RCTs) of adult COVID-19 patients with AHRF who have received APP intervention. Data on the incidence of intubation and mortality were extracted. The odds ratio for intubation and mortality were pooled together using the fixed-effects model. Results: The search identified 1,082 studies from which 17 RCTs involving 3,873 participants were included. Pooled data showed that APP was associated with a 29% decreased likelihood of intubation (OR 0.71 [95% CI: 0.61, 0.84]). The pooled OR for mortality provided some evidence that APP is associated with an 18% decreased likelihood of mortality (OR 82 [95% CI: 0.69, 0.98]). Conclusion: Evidence from this review shows that APP is associated with reduced odds of intubation and mortality in non-intubated patients with COVID-19. We conclude that APP has some benefits in improving morbidity and reducing mortality among patients with COVID-19–related cardiopulmonary compromise.
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.030 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".