Effect of Frailty on Outcome after Traumatic Spinal Cord Injury
Bibliographic record
Abstract
Frailty negatively affects outcome in elective spine surgery populations. This study sought to determine the effect of frailty on patient outcome after traumatic spinal cord injury (tSCI). Patients with tSCI were identified from our prospectively collected database from 2004 to 2016. We examined effect of patient age, admission Total Motor Score (TMS), and Modified Frailty Index (mFI) on adverse events (AEs), acute length of stay (LOS), in-hospital mortality, and discharge destination (home vs. other). Subgroup analysis (for three age groups: <60, 61–75, and 76+ years), and multi-variable analysis was performed to investigate the impact of age, TMS, and mFI on outcome. For the 634 patients, the mean age was 50.3 years, 77% were male, and falls were the main cause of injury (46.5%). On bivariate analysis, mFI, age at injury, and TMS were predictors of AEs, acute LOS, and in-hospital mortality. After statistical adjustment, mFI was a predictor of LOS ( p = 0.0375), but not of AEs ( p = 0.1428) or in-hospital mortality ( p = 0.1245). In patients <60 years of age, mFI predicted number of AEs, acute LOS, and in-hospital mortality. In those aged 61–75, TMS predicted AEs, LOS, and mortality. In those 76+ years of age, mFI no longer predicted outcome. Age, mFI, and TMS on admission are important determinants of outcome in patients with tSCI. mFI predicts outcomes in those <75 years of age only. The inter-relationship of advanced age and decreased physiological reserve is complex in acute tSCI, warranting further study. Identifying frailty in younger patients with tSCI may be useful for peri-operative optimization, risk stratification, and patient counseling.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".