Impact and cost of preoperative computed tomography imaging on the management of patients diagnosed with high‐grade endometrial cancer
Bibliographic record
Abstract
OBJECTIVE: To investigate the cost and impact of routine preoperative computed tomography (CT) in patients with high-grade endometrial cancer, and its role in identifying extrauterine disease. METHODS: We retrospectively identified patients with high-grade endometrial cancer who underwent routine CT scan prior to surgical procedure between September 1, 2005, and January 31, 2015. Cases in which CT findings led to alterations in the treatment plan were documented. Incidental findings unrelated to endometrial cancer diagnosis were captured. Cost of imaging and diagnostic procedures was based on Ontario's Physician Services-Schedule of Benefits. RESULTS: Of 179 patients included, 57 (31.9%) were diagnosed with stage 3-4 disease. CT showed evidence of metastatic disease in 30 (16.8%) patients; however, planned surgical procedure was altered in only nine (5.0%) cases. CT results showed incidental findings requiring follow-up in 78 (43.6%) cases, three of which were second malignancies. We estimate an expenditure of CAD$14 185.85 on routine imaging for every case in which surgical management was changed. CONCLUSIONS: Preoperative CT imaging is efficacious in identifying extrauterine disease in patients with high-grade endometrial cancer, although it seldom alters surgical management. Many of these CT scans will identify incidental findings requiring further interventions, resulting in substantial costs.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".