Meta analysis of effect of tracheostomy timing on prognosis of patients with cervical spinal cord injury
Bibliographic record
Abstract
Objective To evaluate the effects of early tracheostomy and late tracheostomy on the prognosis of patients with cervical spinal cord injury, so as to provide evidence based guidance for the timing of tracheostomy. Methods Relevant literatures studying the timing of tracheostomy in patients with cervical spinal cord injury were searched in PubMed, Embase, Medline, Cochrane Library, Chinese Biological Medical Literature database (CBM), China National Knowledge Infrastructure database (CNKI), and VIP journal database with time range from journal establishment to March 2018. The retrieved articles were screened according to the inclusion and exclusion criteria. The article quality was rigorously evaluated according to the Newcastle-Ottawa scale (NOS). Meta analysis was conducted using Review Manager 5.3 software to compare the mechanical ventilation time, ICU stay, incidence of pneumonia, incidence of complications, and mortality between early and late tracheostomy in patients with cervical spinal cord injury. Results A total of eight articles of cohort study including 466 patients were included , with 241 patients in the early tracheostomy group and 225 patients in the late tracheostomy group. The eight articles were all determined as high quality studies according NOS. The results of Meta analysis showed that there were significant differences between the two groups in terms of the total mechanical ventilation time (MD=-12.28, 95%CI -20.09--4.47, P 0.05) and the incidence of post tracheostomy pneumonia (RR=0.80, 95%CI 0.51-1.26, P>0.05). Conclusion Early tracheostomy can shorten the mechanical ventilation time, ICU stay, incidence of complications, and mortality, but it cannot reduce the incidence of pneumonia. Key words: Tracheotomy; Spinal cord injuries; Prognosis; Meta analysis
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".