Time on wait lists for coronary bypass surgery in British Columbia, Canada, 1991 – 2000
Bibliographic record
Abstract
BACKGROUND: In British Columbia, Canada, all necessary medical services are funded publicly. Concerned with growing wait lists in the mid-1990s, the provincial government started providing extra funding for coronary artery bypass grafting (CABG) operations annually. Although aimed at improving access, it is not known whether supplementary funding changed the time that patients spent on wait lists for CABG. We sought to determine whether the period of registration on wait lists had an effect on time to isolated CABG and whether the period effect was similar across priority groups. METHODS: Using records from a population-based registry, we studied the wait-list time before and after supplementary funding became available. We compared the number of weeks from registration to surgery for equal proportions of patients in synthetic cohorts defined by five registration periods in the 1990s. RESULTS: Overall, 9,231 patients spent a total of 137,126 person-weeks on the wait lists. The time to surgery increased by the middle of the decade, and decreased toward the end of the decade. Relative to the 1991-92 registration period, the conditional weekly probabilities of undergoing surgery were 30% lower among patients registered on the wait lists in 1995-96, hazard ratio (HR) = 0.70 (0.65-0.76), and 23% lower in 1997-98 patients, HR = 0.77 (0.71-0.83), while there were no differences with 1999-2000 patients, HR = 0.94 (0.88-1.02), after adjusting for priority group at registration, comorbidity, age and sex. We found that the effect of registration period was different across priority groups. CONCLUSION: Our results provide evidence that time to CABG shortened after supplementary funding was provided on an annual basis to tertiary care hospitals within a single publicly funded health system. One plausible explanation is that these hospitals had capacity to increase the number of operations. At the same time, the effect was not uniform across priority groups indicating that changes in clinical practice should be considered when adding extra funding to reduce wait lists.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".