The Timing Dilemma: A Systematic Review and Meta-analysis of Short-Term Mortality in COVID-19 Patients Undergoing Tracheostomy with Varied Definitions of Early, Including 7, 10, and 14 Days
Bibliographic record
Abstract
tracheostomy for respiratory failure.Exclusion criteria involved irrelevant publication types, outcomes misaligned with the analysis scope, and unclear or non-compliant timing of tracheostomy. Information sourcesWe systematically searched the following databases: PubMed, Embase, Cochrane Library, Web of Science, and Scopus, using Boolean operators such as AND, OR, or NOT to refine and broaden search results. Main outcome(s) Short-term mortality. Additional outcome(s) Duration of mechanical ventilation; length of ICU stay; hospital day.Quality assessment / Risk of bias analysis Using the Newcastle-Ottawa Scale. Strategy of data synthesisThe study utilized odds ratios for dichotomous outcomes and weighted mean differences for continuous outcomes.Heterogeneity was assessed with I2, applying a random-effects model for significant heterogeneity (I2 ≥ 50%) and a fixed-effects model otherwise.Sensitivity analyses involved stepwise removal of studies, starting with the highest I2.Publication bias was assessed through funnel plots with a significance threshold of P < 0.05.Analyses were performed using Review Manager 5.2 and R version 4.3.1. Subgroup analysisIn the subgroup analysis, "early" was defined based on different time points, specifically at 7 days, 10 days, and 14 days.This stratification allowed for a detailed examination of the impact of early tracheostomy at varying stages in the patient's clinical course. Sensitivity analysisSensitivity analyses were conducted by removing one study at a time, starting with the study with the highest I2, to assess its impact on heterogeneity.
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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 | 0.030 | 0.062 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.042 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".