Evaluating Quality Metrics and Cost After Discharge
Bibliographic record
Abstract
BACKGROUND: Early readmission to hospital after surgery is an omnipresent quality metric across surgical fields. We sought to understand the relative importance of hospital readmission among all health services received after hospital discharge. OBJECTIVE: The aim of this study was to characterize 30-day postdischarge cost and risk of an emergency department (ED) visit, readmission, or death after hospitalization for elective major vascular surgery. METHODS: This is a population-based retrospective cohort study of patients who underwent elective major vascular surgery - carotid endarterectomy, EVAR, open AAA repair, bypass for lower extremity peripheral arterial disease - in Ontario, Canada, between 2004 and 2015. The outcomes of interest included quality metrics - ED visit, readmission, death - and cost to the Ministry of Health, within 30 days of discharge. Costs after discharge included those attributable to hospital readmission, ED visits, rehab, physician billing, outpatient nursing and allied health care, medications, interventions, and tests. Multivariable regression models characterized the association of pre-discharge characteristics with the above-mentioned postdischarge quality metrics and cost. RESULTS: A total of 30,752 patients were identified. Within 30 days of discharge, 2588 (8.4%) patients were readmitted to hospital and 13 patients died (0.04%). Another 4145 (13.5%) patients visited an ED without requiring admission. Across all patients, over half of 30-day postdischarge costs were attributable to outpatient care. Patients at an increased risk of an ED visit, readmission, or death within 30 days of discharge differed from those patients with relatively higher 30-day costs. CONCLUSION: Events occurring outside the hospital setting should be integral to the evaluation of quality of care and cost after hospitalization for major 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 | 0.010 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".