Cost-Effectiveness Analysis of Treatment Strategies for Stage I and II Endometrial Cancer
Bibliographic record
Abstract
OBJECTIVE: Practice patterns vary across Canada with respect to indications for surgical staging and adjuvant radiotherapy in early endometrial cancer. We evaluated the cost-effectiveness of two common strategies for managing early endometrial cancer as part of an Ontario population-based study. METHODS: A decision-analytic model (DATA 4.5) was developed for Stage I and II endometrioid-type cancer using empiric data from Ontario. On the basis of preoperative biopsy grade, one of two surgical procedures was selected: (1) hysterectomy and bilateral salpingo-oophorectomy (HBSO) or (2) surgical staging (HBSO and pelvic +/- para-aortic lymphadenectomy). Adjuvant radiotherapy (RT) was administered according to final grade and stage. After HBSO, pelvic RT was indicated for Grades 1 and 2 if Stage IC, IIA with > 50% myometrial invasion (MI), or IIB, and for Grade 3 if Stage IB, IC, IIA, or IIB. After staging, pelvic RT was indicated for Grades 1 and 2 if Stage IIB, and for Grade 3 if Stage IC, IIA with > 50% MI, or IIB. Main outcome measures were quality-adjusted life-years (QALY) and incremental cost-effectiveness ratios (ICER). Sensitivity analyses were used to evaluate uncertainty around various parameters. RESULTS: The most cost-effective (dominant) strategies were determined for each preoperative grade. For Grade 1, HBSO strongly dominated surgical staging. For Grade 2, neither strategy was dominant; surgical staging had an ICER of $5216 per QALY. For Grade 3, surgical staging strongly dominated HBSO. These results were stable over a wide range of estimates for costs and utilities (i.e., patient preferences for a particular health state). CONCLUSION: The most cost-effective treatment strategies for early endometrial cancer in Ontario differ according to preoperative grade.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".