The Impact of Seasonal Operating Room Closures on Wait Times for Oral Cancer Surgery
Bibliographic record
Abstract
Background: Operating room slowdowns occur at specific intervals in the year as a cost-saving measure. We aim to investigate the impact of these slowdowns on the care of oral cavity cancer patients at a Canadian tertiary care centre. Methods: A total of 585 oral cavity cancer patients seen between 1999 and 2015 at the London Health Science Centre (LHSC) Head and Neck Multidisciplinary Clinic were included in this study. Operating room hours and patient load from 2006 to 2014 were calculated. Our primary endpoint was the wait time from consultation to definitive surgery. Exposure variables were defined according to wait time intervals occurring during time periods with reduced operating room hours. Results: Overall case volume rose significantly from 2006 to 2014 (p < 0.001), while operating room hours remained stable (p = 0.555). Patient wait times for surgery increased from 16.3 days prior to 2003 to 25.5 days in 2015 (p = 0.008). Significant variability in operating room hours was observed by month, with lowest reported for July and August (p = 0.002). The greater the exposure to these months, the more likely patients were to wait longer than 28 days for surgery (odds ratio per day [OR]: 1.07, 95% confidence interval [CI]: 1.05 to 1.10, p < 0.001). Individuals seen in consultation preceding a month with below average operating room hours had a higher risk of disease recurrence and/or death (hazard ratio [HR]: 1.59, 95% CI: 1.10 to 2.30, p = 0.014). Conclusions: Scheduled reductions in available operating room hours contribute to prolonged wait times and higher disease recurrence. Further work is needed to identify strategies maximizing efficient use of health care resources without negatively affecting patient outcomes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".