Quality of preoperative pelvic computed tomography (CT) and magnetic resonance imaging (MRI) for rectal cancer in a region in Ontario: A retrospective population‐based study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Treatment decisions for rectal cancer rely on preoperative staging with CT and MRI scans. We assessed the quality of such scans in a region of Ontario. METHODS: We retrospectively collected data for patients undergoing rectal cancer surgery between July 2011 and December 2014. We measured three aspects of quality: use; comprehensiveness of reporting T-category, N-category, mesorectal fascia (MRF) status; and in non-radiated patients sensitivity and specificity of reports for relevant elements. RESULTS: A total of 559 patients underwent major rectal cancer surgery. Preoperative staging with CT and MRI was performed in 93% and 50% of patients. CT scan reports provided information on T-category, N-category, and MRF status in 41%, 92%, and 16% of cases. These same elements were reported on MRI in 88%, 93%, and 62% of cases. CT scan sensitivity and specificity was 80% and 80% for T-category, and 85% and 39% for N-category. MRI sensitivity and specificity was 75% and 81% for T-category, 79% and 37% for N-category, and 33% and 89% for MRF status. CONCLUSION: In this region of Ontario, pre-operative MRI was underutilized, CT reporting of MRF status was low, and when reported sensitivity and specificity of T- and N-category were similar for CT and MRI.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".