Home care nursing after elective vascular surgery: an opportunity to reduce emergency department visits and hospital readmission
Bibliographic record
Abstract
BACKGROUND: Events occurring outside the hospital setting are underevaluated in surgical quality improvement initiatives and research. OBJECTIVE: To quantify regional variation in home care nursing following vascular surgery and explore its impact on emergency department (ED) visits and hospital readmission. METHODS: Patients who underwent elective vascular surgery and were discharged directly home were identified from population-based administrative databases for the province of Ontario, Canada, 2006-2015. The index surgeries included carotid endarterectomy, open and endovascular aortic aneurysm repair and bypass for lower extremity peripheral arterial disease. Home care nursing within 30 days of discharge was captured and compared across regions. Using multilevel logistic regression, we characterised the association between home care nursing and the risk of an ED visit or hospital readmission within 30 days of discharge. RESULTS: The cohort included 23 617 patients, of whom 9002 (38%) received home care nursing within 30 days of discharge home. Receipt of nursing care after discharge home varied widely across Ontario's 14 administrative health regions (range 16%-84%), even after accounting for differences in patient case mix. A lower likelihood of an ED visit or hospital readmission within 30 days of discharge was observed among patients who received home care nursing following three of four index surgeries: carotid endarterectomy OR 0.74, 95% CI 0.61 to 0.91; endovascular aortic aneurysm repair OR 0.85, 95% CI 0.72 to 0.99; open aortic aneurysm repair OR 1.06, 95% CI 0.91 to 1.23; bypass for lower extremity peripheral arterial disease OR 0.81, 95% CI 0.72 to 0.92. CONCLUSION: Home care nursing may contribute to reducing ED visits and hospital readmission and is variably prescribed after vascular surgery.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".