Acute traumatic quadriplegia in adults: predictors of acute in-hospital mortality
Bibliographic record
Abstract
AIM: To assess the in-hospital mortality rate in adult patients suffering acute traumatic complete quadriplegia and determine the possible predictors of mortality in these patients. MATERIAL AND METHODS: A review of all complete quadriplegics treated from January 1996 through March 2004 in a regional spine injuries unit measuring in-hospital mortality and other factors that might contribute to increased mortality. Multivariate logistic regression analysis was performed to explore these possible predictors of mortality. RESULTS: We identified 126 cases of cervical spinal cord injury treated at our hospital from January 1996 to March 2004 and identified only 62 cases of complete quadriplegia. Of 62 patients, 11 (17.7%) died in the hospital. Age, gender, injury mechanism and medical co-morbidity showed only trends towards a higher mortality. Age and pre-injury medical co-morbidity were found to be significant independent predicting factors for mortality. Gender, mechanism of injury, neurological level and injury severity score were not the predictors of mortality in these patients. CONCLUSION: Despite the limitations of the current evidence, advanced age and pre-existing medical co-morbidity are likely predictors of hospital mortality in the traumatic quadriplegia population.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".