Impact of wait times on survival of women with uterine cancer.
Bibliographic record
Abstract
5586 Background: Reducing cancer wait times have been a priority investment for Cancer Care Ontario since 2005. Our objective was to determine whether wait time from histologic diagnosis of uterine cancer to time of definitive surgery by hysterectomy impacted on all cause survival. Methods: Cases were identified in the Ontario Cancer Registry using ICD-09 codes 179 and 182. Excluded were women without histologic/cytologic confirmation of cancer prior to surgery, with no definitive surgery, or with wait times of ≤14 days or >2 years. Survival was calculated using the Kaplan-Meier method from the day of hysterectomy. Factors were evaluated for their prognostic ability on survival using Cox proportional hazards regression. Wait time was evaluated as a continuous variable and dichotomized at selected cutpoints in the univariable analyses and in a multivariable model adjusting for significant patient factors identified using forward stepwise selection. Results: The final study population included 8,744 women. 51.9% had surgery by a gynaecologist and 69.9% had endometrioid adenocarcinoma. The optimal model is shown below. Multivariable analysis of factors prognostic for survival. Longer wait times remained a statistically significant negative prognostic factor for survival regardless of definition, univariably (p<0.002) and multivariably after adjusting for other significant factors (p<0.001). The final multivariable model is shown. 5-year (95%CI) survival for women with more than 12 week wait times was 61.4 (57.8-64.8)% versus 71.9 (69.9-73.8)% for women with less than 6 week wait time. Conclusions: The longer a woman waits from diagnosis of uterine cancer to definitive surgery negatively impacts her overall survival. [Table: see text]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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 teacher head, 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".