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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.023 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.012 | 0.029 |
| Open science | 0.013 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; both teacher heads agree on what is shown here.
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".