Thirty-day hospital readmission and emergency department visits after vascular surgery: a Canadian prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Rates of hospital readmission following surgery can serve as a marker for quality of care. The aim of this study was to establish the rates and causes of readmission and emergency department visits after vascular surgery and to understand how these patients are managed. METHODS: We conducted a prospective observational cohort study including all inpatients who underwent major vascular surgery between September 2015 and June 2016 at a tertiary vascular care centre in Toronto. Patients were followed at 30 days after discharge via telephone interview. RESULTS: We enrolled 133 patients (94 men [70.7%] and 39 women [29.3%] with a mean age of 65.3 years). The most common index admission diagnosis was peripheral artery disease (67 patients [50.4%]). At 30 days, 19 patients (14.8%) had been readmitted or had visited the emergency department, most commonly after lower extremity revascularization (19.4%). Ten patients were readmitted a mean of 16.8 days following discharge; surgical site infection was the most common cause for readmission (3 patients). The most common treatment was antimicrobial therapy (4 patients). The mean hospital length of stay was 14.4 days. Nine patients presented to the emergency department a mean of 10.6 days after discharge; 6 reported a wound issue, and most (6 of 9) were managed with oral antibiotic treatment. CONCLUSION: Early readmission/emergency department visits after lower extremity revascularization surgery in patients with peripheral artery disease are common and are often due to surgical site infection or wound-related issues. Follow-up within 7-10 days and a specialized wound care team may help reduce the occurrence of these events.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".