Additional file 1 of Tracheostomy timing and outcome in critically ill patients with stroke: a meta-analysis and meta-regression
Bibliographic record
Abstract
Additional file 1. Item S1: PRISMA Checklist; Item S2: Additional Methods (Statistical Analysis) - Summary of Sensitivity Analysis: Villwock et al., (2014); Item S3: Outcomes reported (by study); Item S4: Mean time to Tracheostomy (forest-plot); Item S5: Unadjusted (A) and follow up adjusted overall mortality (B); Item S6: NOS study quality and bias assessment; Item S7: Funnel plots & test of plot asymmetry; Item S8: Ventilator Associated Pneumonia (forest-plot); Item S9: ICU mortality overall estimate; Item S10: Sensitivity analysis (Mortality): Early vs. Late tracheostomy (subgroup); Item S11: Multimodel Interference outputs with Information Criteria (AICc) and Weights (A-E); Item S12: Proportion of good neurological outcome (mRS 0-3, %), Mechanical Ventilation Duration, Hospital Length of Stay, ICU-Length of stay (forest-plot, A-D); Item S13: Meta-regression outputs; Item S14: Additional Results (Mean mRS score); Item S15: Additional Results and Discussion (Mean MV Duration); Item S16: SETPOINT-2 threshold interaction term outputs (Mortality and ICU-LOS); Item S17: Test of correlation between moderator variables.
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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 | 0.007 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.775 | 0.037 |
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".