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 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 | 0.004 |
| 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) | 0.001 | 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".