Timing of recurrences of TEM resected rectal neoplasms is variable as per the surveillance practices of one tertiary care institution
Bibliographic record
Abstract
Transanal endoscopic microsurgery (TEM) is widely used for the excision of rectal adenomas and early rectal adenocarcinoma. Few recommendations currently exist for surveillance of lesions excised by TEM. The purpose of this study was to review the surveillance practices and the patterns of recurrence among TEM resected lesions at a tertiary care hospital. A retrospective chart review was performed on all patients who underwent TEM for rectal adenoma or adenocarcinoma before June 2017. In our study population of 114 patients, the final pathology included 78 (68%) adenomas and 36 (32%) adenocarcinomas. Of the adenocarcinomas 23, 9, and 4 were T1, T2, T3 lesions, respectively. Of those, 25 patients opted for surveillance instead of further treatment. The most commonly recommended endoscopic surveillance strategy by our group for both adenomas and adenocarcinomas excised by TEM was flexible sigmoidoscopy every 6 months for 2 years. Recurrences occurred in 4/78 (5.1%) adenoma patients, all found within 16.9 months of surgery, and in 4/25 (16%) adenocarcinoma patients, found between 7.4 and 38.5 months post-surgery. Our data highlights the fact that the timing of recurrences post TEM surgery is variable. Further studies looking at recurrence patterns are needed in order to create comprehensive guidelines for surveillance of these patients.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".