Functional Decline After Nonhospitalized Injuries in Older Patients: Results From the Canadian Emergency Team Initiative Cohort in Elders
Bibliographic record
Abstract
STUDY OBJECTIVE: To estimate the cumulative incidence of functional decline over 6 months following emergency department (ED) assessments of nonhospitalized injuries and to identify its main determinants. METHODS: We conducted a prospective multicenter cohort of older adults discharged home following assessment for injuries in 8 Canadian EDs. Participants were assessed at 3 time points: baseline in the ED, 3 months, and 6 months. The primary outcome, functional decline, was defined as a 2-points loss from baseline on the Older American Resources Scale (OARS). Other measures included demographics, comorbidities, injury characteristics, frailty, cognition, mobility status, etc. Cumulative incidences were estimated using proportions with 95% confidence intervals. Log-binomial regressions and the "least absolute shrinkage and selection operator" (LASSO) were used to identify significant functional decline determinants. RESULTS: Among 2,919 participants, 403 (13.8%) were lost to follow-up. Mean age was 76.2±7.6 years, 65.3% were women, 9% were frail, and 40.0% prefrail. Main injury mechanisms were falls (65.5%) and motor vehicle accidents (18.6%). The cumulative incidence of functional decline over 6 months was 17.0% (95% confidence interval 12.5% to 23.0%). Occasional use of walking devices, less than 5 outings/week, frailty, and older age were significant baseline determinants of functional decline. CONCLUSION: A significant 17% of older adults with "minor" injuries experience a persistent functional decline over 6 months following their ED visit. Four frailty-related determinants were identified: occasional use of a walking device, less than 5 outings/week, frailty, and older age. Further work is needed to assess if these can help ED clinicians screen seniors at risk and initiate interventions at discharge.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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".