Transanal endoscopic microsurgery for rectal villous tumours: Can we rely solely on preoperative biopsies and the surgeon’s experience?
Bibliographic record
Abstract
Background: Transanal endoscopic microsurgery has become the standard of treatment for rectal villous adenomas. However, the role of preoperative imaging for these lesions is not clear. The aim of this study was to compare the value of preoperative imaging and surgeon clinical staging in the preoperative evaluation of patients with rectal villous adenomas having transanal endoscopic microsurgery resection. Methods: We conducted a single-centre comparative retrospective cohort study of patients who underwent transanal endoscopic microsurgery surgery for rectal villous adenomas from 2011 to 2013. The intervention was preoperative imaging versus surgeon clinical staging. The primary outcome was the accuracy of clinical staging by preoperative imaging and surgeon clinical staging according to the histopathologic staging. Results: A total of 146 patients underwent transanal endoscopic microsurgery surgery for rectal villous adenomas. One hundred and twelve (76.7%) of those patients had no preoperative imaging while 34 patients (23.3%) had either endorectal ultrasound (22 patients) or magnetic resonance imaging (12 patients). Surgeon staging was accurate in 89.3% of cases whereas staging by endorectal ultrasound was accurate in 40.9% cases and magnetic resonance imaging was accurate in 0% of cases. In the imaging group, inaccurate staging would have led to unnecessary radical surgery in 44.0% of patients. Conclusion: This study was subject to selection bias because of its retrospective nature and the limited number of patients with imaging. Patients with rectal villous tumours without invasive carcinoma on biopsies and without malignant characteristics on appearance in the judgment of an experienced colorectal surgeon might not benefit from preoperative imaging before undergoing transanal endoscopic microsurgery procedures.
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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 | 0.010 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".