Management of rectal neuroendocrine tumours by transanal endoscopic microsurgery
Bibliographic record
Abstract
AIM: The objective of this study was to evaluate the safety and effectiveness of transanal endoscopic microsurgery for rectal neuroendocrine tumours. METHOD: A retrospective cohort study of all pathology-confirmed rectal neuroendocrine tumours treated by transanal endoscopic microsurgery from April 2007 to December 2020 at a tertiary care centre was performed. Demographic, clinical, radiographic and pathological data were collected. Characteristics of patients with recurrence were examined. Descriptive statistics were performed. RESULTS: There were 58 patients treated by transanal endoscopic microsurgery excision. Referrals were for primary excision (15, 25.9%), completion re-excision after incomplete endoscopic removal (38, 65.5%) or locally recurrent rectal neuroendocrine tumours (5, 8.6%). The mean age of patients was 56.4 ± 11.9 years and 26 patients were women (44.8%). Mean tumour size was 7.4 ± 3.8 mm (range 1.0-15.0 mm). Most (86.4%) were Grade 1 tumours. Mean operative time was 37.2 ± 17.2 min and 56 patients (96.6%) were discharged on the same day. All patients had negative margins on final pathology. Of the 38 patients who were referred for completion re-excision after incomplete endoscopic removal, eight (21.1%) had residual tumour on final pathology. Three recurrences were diagnosed at 2.1, 4.5 and 12.5 years after excision. All recurrences were from Grade 1 or 2 primary tumours, less than 2 cm, and diagnosed radiographically. CONCLUSION: To date, this is the largest North American study looking at transanal endoscopic microsurgery for rectal neuroendocrine tumours. This technique is effective in managing primary, incompletely excised and recurrent tumours with good clinical and oncological outcomes.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".