Primary care follow-up improves outcomes in older adults following emergency general surgery admission
Bibliographic record
Abstract
BACKGROUND: While preoperative optimization improves outcomes for older adults undergoing major elective surgery, no such optimization is possible in the emergent setting. Surgeons must identify postoperative interventions to improve outcomes among older emergency general surgery (EGS) patients. The objective of this cohort study was to examine the association between early follow-up with a primary care physician (PCP) and the risk of nursing home acceptance or death in the year following EGS admission among older adults. METHODS: Using population-based administrative health data in Ontario, Canada (2006-2016), we followed all older adults (65 years or older) for 1 year after hospital admission for EGS conditions. A multivariable Cox model was used to identify the association between early postdischarge follow-up with a patient's PCP and the time to nursing home acceptance or death while adjusting for confounders. RESULTS: Among 76,568 older EGS patients, 32,087 (41.9%) were seen by their usual PCP within 14 days of discharge, and 9,571 (12.5%) were accepted to a nursing home or died within 1 year. Primary care physician follow-up was associated with a 13% reduced risk of nursing home acceptance or death compared with no follow-up (hazard ratio 0.87; 95% confidence interval 0.84-0.91). This effect was consistent across age and frailty strata, patients managed operatively and nonoperatively, and patients who had both high and low baseline continuity of care with their PCP. CONCLUSION: Early follow-up with a familiar PCP was associated with a reduced risk of nursing home acceptance or death among older adults following EGS admission. Structures and processes of care are needed to ensure that such follow-up is routinely arranged at discharge. LEVEL OF EVIDENCE: Therapeutic/Care Management; Level III.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".