Early versus Late Tracheostomy
Bibliographic record
Abstract
OBJECTIVE: To investigate whether early tracheostomy leads to improved outcomes compared with late tracheostomy. DATA SOURCES: Ovid MEDLINE (including PubMed), Embase, and the Cochrane Central Register of Controlled Trials. REVIEW METHODS: A systematic search was performed of the above-mentioned databases according to PRISMA guidelines. Data were collected on the following outcomes of interest: hospital mortality, intensive care unit length of stay, length of mechanical ventilation, incidence of pneumonia, laryngotracheal injury, and sedation use. Analysis was performed using the RevMan 5 software (Cochrane Collaboration, Oxford, England). RESULTS: Eleven studies were included for analysis. There was a significant decrease in the intensive care unit length of stay in the early tracheostomy group (weighted mean difference, -9.13 days; 95% confidence interval [CI], -17.55 to -0.70; P = .03). There was no significant difference in hospital mortality (relative risk, 0.84; 95% CI, 0.67 to 1.04; P = .11). A pooled analysis was not performed for the incidence of pneumonia or length of mechanical ventilation, secondary to considerable heterogeneity among the studies. None of the studies reporting laryngotracheal outcomes found a significant difference between the early and late tracheostomy groups, whereas all 3 studies reporting sedation use found a significant decrease in the early tracheostomy group. CONCLUSION: Early tracheostomy performed within 7 days of intubation was associated with a decrease in intensive care unit length of stay. No difference was found in hospital mortality. Insufficient data currently exist to make conclusions about the effect of early tracheostomy on the incidence of pneumonia, length of mechanical ventilation, laryngotracheal injury, or sedation use.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".