Comparison of Age and Modified Frailty Index-5 as Predictors of In-Hospital Mortality in Complete Traumatic Cervical Spinal Cord Injury
Bibliographic record
Abstract
Abstract Frailty, as measured by the modified frailty index-5 (mFI-5), and older age are associated with increased mortality in the setting of spinal cord injury (SCI). However, a comparison of the predictive power of each measure has not been completed. We conducted a retrospective cohort study to evaluate in-hospital mortality among adult complete cervical SCI patients at participating centers of the Trauma Quality Improvement Program from 2010 to 2018. Logistic regression was used to predict in-hospital mortality, and the area under the Receiver Operating Characteristic curve (AUROC) of regression models with age, mFI-5, or age with mFI-5 was used to compare predictive power. 4,733 patients were eligible. We found significant effect of age > 75 years (OR 9.77 95% CI [7.21 13.29]) and mFI-5 ≥ 2 (OR 3.09 95% CI [1.85 4.99]) on in-hospital mortality. The AUROC of a model including age and mFI-5 (0.81 95%CI [0.79 0.84] AUROC) was comparable to a model with age alone (0.81 95%CI [0.79 0.83] AUROC). Both models were superior to a model with mFI-5 alone (0.75 95% CI [0.72 0.77] AUROC)). Our findings suggest that age provides more predictive power than mFI-5 in the prediction of in-hospital mortality for complete cervical SCI.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".