Variation in time spent on the waiting list for elective vascular surgery: a case study.
Bibliographic record
Abstract
OBJECTIVE: To review the variation in time spent on the waiting list for elective vascular surgery provided by a single team of specialists. DESIGN: A prospective cohort study. SETTING: An acute care hospital in Ontario. POPULATION: One thousand and eighty-four consecutive patients with vascular problems accepted for elective surgery between 1994 and 1998. INTERVENTIONS: Abdominal aortic aneurysm (AAA) repair; carotid endarterectomy (CAD); surgery for peripheral vascular disease (PVD); and arteriovenous fistula (AVF) for long-term access in patients with renal failure. OUTCOME MEASURES: Time-to-treatment curves, admission rates. RESULTS: The weekly admission rate was 9.8% on average. The proportion of patients who underwent operation was 50% at 7 weeks, 75% at 14 weeks and 90% at 26 weeks. The weekly admission rate varied according to clinical priority, from 42% in priority class 1 to 6% in class 5. In any priority class, the admission rate was not constant over time. Although the proportion of patients operated on within the maximum recommended time in classes 1, 2, 3 and 4 was 52%, 50%, 35% and 20% respectively, the last 10% of patients waited 5 to 16 weeks, 10 to 16 weeks, 16 to 37 weeks, and 25 to 39 weeks respectively. There were statistically significant differences in waiting time by surgical procedure among the least urgent cases, with median times of 7, 10 and 19 weeks for AVF, PVD and CAD procedures, respectively. CONCLUSIONS: When queuing procedures are uniform, the waiting times for access to elective vascular surgery provided by the same team of specialists differ considerably for patients with equal surgical needs and urgency. It remains to be examined whether delays in scheduling operations and cancellations affect the waiting time after adjustment for urgency and comorbidity.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".