Additional file 1 of Impact of exposure time in awake prone positioning on clinical outcomes of patients with COVID-19-related acute respiratory failure treated with high-flow nasal oxygen: a multicenter cohort study
Bibliographic record
Abstract
Additional file 1: 1. Further details on the procedures. 2. Further details on statistical analysis. TABLE E1. Baseline characteristics of the study population and the balance between groups. TABLE E2. Treatment with oxygen therapy and prone positioning. TABLE E3. Outcomes of patients in awake prone positioning versus non awake prone positioning. TABLE E4. Risk of intubation in awake prone positioning versus non awake prone positioning. OR indicates odds ratio; 95% CI indicates confidence interval. Non-prone positioning group as reference. TABLE E5. Risk of hospital mortality in awake prone positioning versus non-prone positioning. TABLE E6. Functional outcomes at discharge. TABLE E7. Variables related to invasive mechanical ventilation in ventilated patients at day 1 after starting invasive mechanical ventilation. TABLE E8. Selection of variables for adjustment of confounding. Figure E1. Diagnosis of inverse probability weights-propensity score (graphic and statistical). Figure E2. Standardized differences before and after applying inverse probability weighting. Figure E3. Directed acyclic graph (DAG). Figure E4. E-value calculation for primary outcome of interest (ETI). Figure E5. Risk of intubation between groups in the awake prone position vs. non-awake prone position according to severity of respiratory failure. Figure E6. Risk of intubation between groups in the awake prone position vs. non-awake prone position according to predominant body position. Figure E7. Cumulative incidence of endotracheal intubation over time in the study population.
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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.663 | 0.038 |
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".