Tracheostomy in critically ill patients with SARS 2 COVID-19 infection: a prospective observational multi-center study of shortand long-term outcomes
Bibliographic record
Abstract
Background We report the characteristics, timing, and factors related to the decision to perform a tracheostomy in patients with confirmed COVID-19 infection admitted to eight Italian intensive care units (ICUs). Materials and methods Prospective observational cohort study of patients with COVID-19 disease on mechanical ventilation. Long-term functional impairment (up to 180 days' post-hospital discharge) was assessed using the Karnofsky scale. Kaplan--Meier analysis assessed differences in survival and freedom from tracheostomy in relation to ICU stay. Cox regression model was used to assess which variables impacted on tracheostomy as a categorical outcome. Results A total of 248 patients were recruited in the eight participating ICUs. Patients undergoing tracheostomy (n = 128) had longer ICU (25 (18--36) vs. 10 (7--16), P = 0.001) and hospital (37 (26.5--50) vs. 19 (8.5--34.5) P = 0.02) stays. ICU and hospital mortality of patients tracheostomized was 34% and 37%, respectively. Cumulative survival Kaplan--Meier analysis documented improved survival rates in patients undergoing tracheostomy (Log-Rank, Mantel--Cox = 4.8, P = 0.028). Median Karnofsky scale values improved over time but were similar between survivors receiving or not receiving tracheostomy. No healthcare worker involved in the tracheostomy procedure developed COVID-19 infection during the study period. Conclusions Patients with COVID-19 infection who underwent tracheostomy had a better cumulative survival but similar long-term functional outcomes at 30, 60, and 180 days after hospital discharge.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".