Early primary care follow-up is associated with improved long-term functional outcomes among injured older adults
Bibliographic record
Abstract
BACKGROUND: Older adults who survive injury frequently experience functional decline, and interventions preventing this decline are needed. We therefore evaluated the association between early primary care physician (PCP) follow-up and nursing home admission or death among injured older adults. METHODS: We performed a retrospective, population-based cohort study of community-dwelling older adults (65 years or older) discharged alive after injury-related hospitalization (2009-2020). The exposure of interest was early PCP visit (within 14 days of discharge). The primary outcome was time to death or nursing home admission in the year after discharge. Cox proportional hazards models were used to evaluate the relationship between early PCP visit and this outcome, adjusting for baseline characteristics. RESULTS: Among 93,482 patients (63.7% female; mean age, 79.8 years), 24,167 (25.9%) had early follow-up with their own PCP and 6,083 (6.5%) with a different PCP. In the year after discharge, 16,676 patients (17.8%) died or were admitted to a nursing home. After risk adjustment, early follow-up with one's own PCP was associated with a 15% reduction in the hazard of death or nursing home admission relative to no follow-up (hazard ratio, 0.85; 95% confidence interval, 0.83-0.87). Follow-up with a different PCP was not associated with the outcome (hazard ratio, 0.99; 95% confidence interval, 0.95-1.03). These relationships were consistent across all age, sex, frailty, and injury severity strata. CONCLUSION: Among injured older adults, early follow-up with their own PCP was associated with increased time alive and at home. These findings suggest strategies to integrate PCPs into postinjury care of older adults should be explored. LEVEL OF EVIDENCE: Therapeutic/Care Management; Level IV.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".