P.147 The growing problem of spine surgery wait times in British Columbia: longitudinal trends and impacts on perioperative outcomes
Bibliographic record
Abstract
Background: Surgical delays are in common in Canada. Wait times in elective spine surgery and their impact on outcomes remain uncharacterized. Methods: This was a single-center analysis of elective spine surgery data between 2009-2020. Wait times between referral and consultation (T1), consultation and surgical booking (Ti), and booking and surgery (T2) were assessed. Results: 2041 patients were included. Longitudinal analyses were adjusted for age, sex, diagnosis, surgical volume, while outcomes analyses were age and sex-adjusted. Total T1+Ti+T2 increased 8.1% annually (p<0.001). T1 decreased 4.3% annually (p=0.032). It was not associated with adverse events (AEs) or disposition. Every 100 days of T1 was associated with 1.0% longer hospitalization (p=0.001). Ti increased 21.0% annually (p<0.001). Every 100 days of Ti was associated with 2.9% increased odds of an adverse event (p=0.002), 1.8% longer hospitalization (p<0.001), and 15.9% increased likelihood of discharge home (p<0.001). T2 increased 7.0% annually (p<0.001) and was not associated with AEs. Every 100 days of T2 was associated with 11.6% longer hospitalization (p<0.001) and 76.5% increased likelihood of discharge home (p<0.001). Conclusions: Total wait times for elective spine surgery have increased between 2009-2020. Notably, Ti increased ninefold and was associated with AEs. This study highlights areas of delay and targets for healthcare optimization.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".