The wait time creep: Changes in the surgical wait time for women with uterine cancer in Ontario, Canada, during 2000–2009
Bibliographic record
Abstract
OBJECTIVE: Uterine cancer is a major cancer of women, with outcomes potentially worsening with delayed diagnosis or hysterectomy, the main treatment. Yet cancer surgery wait times are not reported by cancer site. This study sought to examine changes in wait times for uterine cancer surgery between 2000 and 2009 and to identify predictors of longer surgery wait times. METHODS: Population-based retrospective analysis of a cohort of uterine cancer patients diagnosed between April 2000 and March 2009. Using linked administrative data, all cases in which a patient had hysterectomy following diagnosis were identified. Wait time was defined as days from diagnosis of uterine cancer (day 0) to hysterectomy. Regression analysis was used to examine the relationship between covariates and wait time. RESULTS: Wait times increased steadily between 2000 and 2006 from a median of 34 to 54 days, followed by a plateau until 2009-during which patients waited a median of between 53 and 55 days for surgery after diagnosis. Overall, 55% of patients had a wait time longer than 6 weeks after diagnosis. Predictors of a wait time greater than 6 weeks included older age, region, lower income, later year of diagnosis, surgery by a gynaecologic oncologist, non-sarcoma histology group and having surgery in a teaching hospital. CONCLUSION: Over half of uterine cancer patients waited longer than the recommended 6 weeks for surgery. Future reporting of cancer wait times by each disease site regularly would help to identify progress to reduce wait times and opportunities for improvement.
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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 | 0.002 |
| 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) | 0.002 | 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".