Measuring Frailty Can Help Emergency Departments Identify Independent Seniors at Risk of Functional Decline After Minor Injuries
Bibliographic record
Abstract
BACKGROUND: This study aims to (i) describe frailty in the subgroup of independent community-dwelling seniors consulting emergency departments (EDs) for minor injuries, (ii) examine the association between frailty and functional decline 3 months postinjury, (iii) ascertain the predictive accuracy of frailty measures and emergency physicians' for functional decline. METHOD: Prospective cohort in 2011-2013 among 1,072 seniors aged 65 years or older, independent in basic daily activities, evaluated in Canadian EDs for minor injuries.Frailty was assessed at EDs using the Canadian Study of Health and Aging-Clinical Frailty scale (CSHA-CFS) and the Study of Osteoporotic Fracture frailty index (SOF). Functional decline was defined as a loss ≥2/28 on the Older American Resources Services scale 3 months postinjury. Generalized mixed models were used to explore differences in functional decline across frailty levels. Areas under the receiver operating characteristic curve were used to ascertain the predictive accuracy of frailty measures and emergency physicians' clinical judgment. RESULTS: The SOF and CSHA-CFS were available in 342 and 1,058 participants, respectively. The SOF identified 55.6%, 32.7%, 11.7% patients as robust, prefrail, and frail. These CSHA-CFS (n = 1,058) proportions were 51.9%, 38.3%, and 9.9%. The 3-month incidence of functional decline was 12.1% (10.0%-14.6%). The Areas under the receiver operating characteristic curves of the CSHA-CFS and the emergency physicians' were similar (0.548-0.777), while the SOF was somewhat higher (0.704-0.859). CONCLUSION: Measuring frailty in community-dwelling seniors with minor injuries in EDs may enhance current risk screening for functional decline. However, before implementation in usual care, feasibility issues such as inter-rater reliability and acceptability of frailty tools in the EDs have to be addressed.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".