Tracheostomy practices in children on mechanical ventilation: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To evaluate current practices of tracheostomy in children regarding the ideal timing of tracheostomy placement, complications, indications, mortality, and success in decannulation. SOURCE OF DATA: The authors searched PubMed, Embase, Cochrane Library, Google Scholar, and complemented by manual search. The guidelines of PRISMA and MOOSE were applied. The quality of the included studies was evaluated with the Newcastle-Ottawa Scale. Information extracted included patients' characteristics, outcomes, time to tracheostomy, and associated complications. Odds ratios (ORs) with 95% CIs were computed using the Mantel-Haenszel method. SYNTHESIS OF DATA: Sixty-six articles were included in the qualitative analysis, and 8 were included in the meta-analysis about timing for tracheostomy placement. The risk ratio for "death in hospital outcome" did not show any benefit from performing a tracheostomy before or after 14 days of mechanical ventilation (p = 0.49). The early tracheostomy before 14 days had a great impact on the days of mechanical ventilation (-26 days in mean difference, p < 0.00001). The authors also found a great reduction in hospital length of stay (-31.4 days, p < 0.008). For the days in PICU, the mean reduction was of 14.7 days (p < 0.007). CONCLUSIONS: The meta-analysis suggests that tracheostomy performed in the first 14 days of ventilation can reduce the time spent on the ventilator, and the length of stay in the hospital, with no effect on mortality. The decision to perform a tracheostomy early or late may be more dependent on the baseline disease than on the time spent on ventilation .
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.037 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".