Canadian Study of Health and Aging Clinical Frailty Scale: Does It Predict Adverse Outcomes among Geriatric Trauma Patients?
Bibliographic record
Abstract
BACKGROUND: The Canadian Study of Health and Aging Clinical Frailty Scale (CFS) and the laboratory Frailty Index (FI-lab) are validated tools based on clinical and laboratory data, respectively. Their utility as predictors of geriatric trauma outcomes is unknown. Our primary objective was to determine whether pre-admission CFS is associated with adverse discharge destination. Secondary objectives were to evaluate the relationships between CFS and in-hospital complications and between admission FI-lab and discharge destination. STUDY DESIGN: We performed a 4-year (2011 to 2014) retrospective cohort study with patients 65 years and older admitted to a level I trauma center. Admission FI-lab was calculated using 23 variables collected within 48 hours of presentation. The primary outcome was discharge destination, either adverse (death or discharge to a long-term, chronic, or acute care facility) or favorable (home or rehabilitation). The secondary outcome was in-hospital complications. Multivariable logistic regression was used to evaluate the relationship between CFS or FI-lab and outcomes. RESULTS: There were 266 patients included. Mean age was 76.5 ± 7.8 years and median Injury Severity Score was 17 (interquartile range 13 to 24). There were 260 patients and 221 patients who had sufficient data to determine CFS and FI-lab scores, respectively. Pre-admission frailty as per the CFS (CFS 6 or 7) was independently associated with adverse discharge destination (odds ratio 5.1; 95% CI 2.0 to 13.2; p < 0.001). Severe frailty on admission, as determined by the FI-lab (FI-lab > 0.4), was not associated with adverse outcomes. CONCLUSIONS: Pre-admission clinical frailty independently predicts adverse discharge destination in geriatric trauma patients. The CFS may be used to triage resources to mitigate adverse outcomes in this population. The FI-lab determined on admission for trauma may not be useful.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".