The Use of Preoperative Imaging in Endometrial Cancer : Are Ontario Physicians Following Goc Guidelines?
Bibliographic record
Abstract
Introduction / Background: Joint SOCG-GOC-SCG clinical practice guidelines recommend against preoperative imaging studies (such as CT), except for cases of locally advanced disease or aggressive histologies. This study utilized population-based data to evaluate the use of preoperative imaging in Ontario and factors associated with its use.Methodology: Ontario women diagnosed with endometrial cancer from 2006-2016 were identified from the Ontario Cancer Registry. Patients with a hysterectomy prior to the date of diagnosis, non- epithelial histology or a prior cancer diagnosis within 5 years were excluded. Age-standardized incidence rate of endometrial cancer and preoperative imaging (CT or MRI) rates were calculated over time. Predictive factors for preoperative imaging use were determined using multi-variable analysis. A subanalysis was performed on low-risk patients, to determine if preoperative imaging use differed in this population.Results: 21641 cases were included for analysis. From 2006-2016, the number of cases of endometrial cancer increased 48.0%, and age-standardized incidence rate increased 17.6%. The use of preoperative imaging increased from 19.3% to 32.6%. In a subanalysis of a low-risk population, the rate of preoperative imaging was 16%, compared with 26.8% in the combined population. Factors most predictive of preoperative imaging were: non-endometrioid histology, higher stage, higher grade, comorbidity score and rurality score.Conclusion: Endometrial cancer incidence and the use of preoperative imaging has increased from 2006 - 2016 in Ontario. Guideline compliance was moderate; while preoperative imaging use was associated with high risk features, inappropriate use was still seen in the low risk population
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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 | 0.001 | 0.009 |
| 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 | 0.001 |
| 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".