Rectal Endoscopic Ultrasound and Staging of Rectal Carcinoma: A Community Perspective
Bibliographic record
Abstract
Introduction: Rectal endoscopic ultrasound (EUS) has become a valuable tool for the pre-operative staging of rectal carcinoma. However, most studies to date have been small prospective or retrospective reviews at large academic centers. We performed a large retrospective review at our community hospital in order to compare our results with the published literature. We believe our results will prove valuable as rectal EUS becomes more established at larger community hospitals in Canada and the United States. Methods: Patient charts were reviewed retrospectively. Inclusion criteria for this study were patients with histologically proven rectal adenocarcinoma who underwent rectal EUS. Exclusion criteria were patients who responded to neoadjuvant therapy. A total of 248 consecutive patients underwent rectal EUS during this time period, of which 150 were included in the study. Endoscopic tumor and nodal staging were compared to the pathological specimen, which served as our gold standard. Results: Overall accuracy for tumor and nodal staging of rectal adenocarcinoma were 73% (95% confidence interval [CI] 64-80%) and 71% (95% CI 62-78%), respectively. When early (T1 and T2) and late stages (T3 and T4) were grouped the accuracy was found to be 85% (95% CI 77-90%). Both tumor and nodal staging accuracy were within the range of reported values in the literature. Conclusion: Our results indicate that rectal EUS provides an effective way to stage rectal adenocarcinoma in a community hospital setting for tumor depth, particularly when early and late tumor stages are grouped together. Rectal EUS was, however, less accurate for staging tumor involvement in perirectal nodes. More cases are required for T1 and T4 tumors in order to better characterize the accuracy of rectal endoscopic ultrasound for these stages at our institution.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".