Prone positioning in awake patients without ventilatory support does not alter major clinical outcomes in severe COVID-19: results from a retrospective observational cohort study, systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: During the Coronavirus disease (COVID-19) pandemic, clinicians recommended awake-prone positioning (APP) to avoid the worst outcomes. The objectives of this study were to investigate if APP reduces intubation, death rates, and hospital length of stay (HLOS) in acute COVID-19. METHODS: We performed a retrospective cohort with non-mechanically ventilated patients hospitalized in a reference center in Manaus, Brazil, 2020. Participants were stratified into APP and awake-not-prone positioning (ANPP) groups. Also, we conducted a systematic review and performed a meta-analysis to understand if this intervention had different outcomes in resource-limited settings (PROSPERO CRD42023422452). RESULTS: A total of 115 participants were allocated into the groups. There was no statistical difference between both groups regarding time to intubation (HR: 0.861; 95CI: 0.474-1.1562; p=0.622) and time to death (HR: 1.666; 95CI: 0.939-2.951; p=0.081). APP was not significantly associated with reduced HLOS. A total of 86 articles were included in the systematic review, of which 76 (88,3%) show similar findings after APP. Also, low/middle, and high-income countries were similar regarding such outcomes. CONCLUSION: APP in COVID-19 does not present clinical improvement that affects mortality, intubation rate and HLOS. The lack of a prone position protocol, obtained through a controlled study, is necessary. After 3 years, APP benefits are still inconclusive.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.024 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".