Does Wait‐List Size at Registration Influence Time to Surgery? Analysis of a Population‐Based Cardiac Surgery Registry
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Bibliographic record
Abstract
OBJECTIVE: To determine whether the probability of undergoing coronary bypass surgery within a certain time was related to the number of patients on the wait list at registration for the operation in a publicly funded health system. METHODS: A prospective cohort study comparing waiting times among patients registered on wait lists at the hospitals delivering adult cardiac surgery. For each calendar week, the list size, the number of new registrations, and the number of direct admissions immediately after angiography characterized the demand for surgery. RESULTS: The length of delay in undergoing treatment was associated with list size at registration, with shorter times for shorter lists (log-rank test 1,198.3, p<.0001). When the list size at registration required clearance time over 1 week patients had 42 percent lower odds of undergoing surgery compared with lists with clearance time less than 1 week (odds ratio [OR] 0.58 percent, 95 percent, confidence interval [CI] 0.53-0.63), after adjustment for age, sex, comorbidity, period, and hospital. The weekly number of new registrations exceeding weekly service capacity had an independent effect toward longer service delays when the list size at registration required clearance time less than 1 week (OR 0.56 percent, 95 percent CI 0.45-0.71), but not for longer lists. Every time the operation was performed for a patient requiring surgery without registration on wait lists, the odds of surgery for listed patients were reduced by 6 percent (OR 0.94, CI 0.93-0.95). CONCLUSION: For wait-listed patients, time to surgery depends on the list size at registration, the number of new registrations, as well as on the weekly number of patients who move immediately from angiography to coronary bypass surgery without being registered on a wait list. Hospital managers may use these findings to improve resource planning and to reduce uncertainty when providing advice on expected treatment delays.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it