Waiting time in relation to wait-list size at registration: statistical analysis of a waiting-list registry.
Bibliographic record
Abstract
OBJECTIVE: To examine the relationship between the length of a waiting list for elective vascular surgery and the delay before undergoing the operation. METHODS: We undertook a prospective cohort study of patients registered on the waiting list for elective vascular surgery at an acute care hospital in Ontario. Regression analysis of wait times to express the admission rate in one group relative to another, with the ratio of rates being a measure of the difference between groups. RESULTS: List length at registration was associated with length of wait (log-rank test 596.4, p < 0.0001). Patients who were registered when the list length exceeded the weekly service capacity had 70% lower conditional probability of undergoing surgery than those on a list with fewer patients (rate ratio 0.30, 95% confidence interval [CI] 0.26-0.36) after adjustment for sex, age, procedure and period. Registering more than 5 patients when the list was short had an independent effect (rate ratio 0.61, CI 0.45-0.82). CONCLUSIONS: The number of registrants on a surgical wait list has an effect on the length of delay in providing necessary treatment. Our results suggest that a regulated list-length policy may contribute to reducing waiting times. Hospital managers may also use the findings to reduce uncertainty in reporting expected waits given the current list size, thereby improving resource planning.
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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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".