Predicting the need for tracheostomy in patients with cervical spinal cord injury
Bibliographic record
Abstract
BACKGROUND: Approximately 75% of hospitalized patients with a cervical spinal cord injury (CSCI) will require intubation and mechanical ventilation (MV) because of compromised respiratory function. It is difficult to predict those CSCI patients who will require prolonged ventilation and therefore will most benefit from early tracheostomy. This study intended to show the benefits of tracheostomy, particularly early, and to identify predictors of prolonged MV after CSCI. METHODS: A retrospective review of patients aged 16 years and older with acute CSCI admitted to London Health Science Center from 1991 to 2010 was performed. Demographic data and clinical parameters were extracted from medical records and the trauma registry. Regression analysis was used to identify predictors of prolonged MV. RESULTS: There were 66 eligible patients of which 42 (62%) had a tracheostomy performed. Five patients (7.6%) remained ventilator dependent and seven (10.6%) died more than 7 days after injury secondary to sepsis. After adjusting for the number of ventilator days after injury, patients who had a tracheostomy had fewer pulmonary complications than those who did not have a tracheostomy (p = 0.001). Early tracheostomy resulted in fewer days on the ventilator and a shorter hospital stay. Clinical parameters that predicted MV to be required longer than 7 days were Injury Severity Score > 32, complete SCI, and a PAO2/FIO2 ratio < 300 3 days after MV was initiated. CONCLUSION: We recommend early tracheostomy if the Injury Severity Score is >32, the patient has a complete SCI, and the PAO2/FIO2 ratio is <300 3 days after MV was initiated. LEVEL OF EVIDENCE: Prognostic study, level III.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".