Patterns of surgical care for uterine cancers in Ontario.
Bibliographic record
Abstract
OBJECTIVES: To facilitate the planning of future resources for cancer services in Ontario, Cancer Care Ontario commissioned an evaluation of operative services delivered for uterine cancer. METHODS: Women with an incident diagnosis of a uterine malignancy were identified from 1 April 2003 to 31 March 2004 using the Ontario Cancer Registry. Record linkages were created to other provincial health databases such as the Ontario Health Insurance Plan. RESULTS: Uterine cancer affected 1,436 women. Disease specific rates of cancer were higher in rural areas and those from the highest income quintiles. Surgery occurred in 94.7% of women. Use of surgery did not appear to vary by SEC, urban/rural residence or LHIN. Gynecologists conducted 76.1% of the operations. Lymphadenectomy took place in 18.7% of women. Lymphadenectomy rates were highest in gynecologic oncologists (43.3%). All women were assessed by CXR. Radiation therapy consults were preformed in half of the women with uterine cancer but treatment was only delivered in half of those seen. Medical oncologists saw about 6.3% of women with uterine cancers. CONCLUSIONS: There appear to be variations in incidence rates of uterine cancer with disease being more frequent in those of the highest SES. In two-thirds of the population, surgery is delivered in the region where the patient lives. Subspecialty care from gynecologic oncologists was provided to one-third of women. Rates of lymphadenectomy as part of a surgical attempt to assess disease spread appear low. These pilot data would be enhanced with further information such as comorbidity, treatment intent (palliative/curative), histology, grade and stage.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".