The total mesorectal excision specimen for rectal cancer: a review of its pathological assessment
Bibliographic record
Abstract
Total mesorectal excision (TME) refers to the surgical removal of the complete perirectal soft tissue envelope, using sharp instruments under direct vision, and has become the contemporary standard of care for patients with rectal cancer. Pathologists play a key role in the evaluation of these specimens, including the quality assurance of surgical performance, as well as evaluation of the circumferential radial margin (CRM). While the latter is the most significant predictor of local recurrence, the quality of the excised mesorectum is another important factor in assessing the risk of local recurrence in patients with a negative CRM. Since proper pathological assessment of the TME specimen provides important prognostic information, as well as critical feedback to surgeons regarding technical performance, it is important to have adequate guidelines for the macroscopic handling of these specimens. The CLASSICC study of the Medical Research Council in the United Kingdom, as well as the Dutch TME trial have introduced a new standard for the pathological assessment of TME specimens, including an approach that involves assessment in both the fresh and fixed states, at least 48 hours of fixation of an intact specimen, with observations made on both the external appearance and cross-sectional slices. This article reviews the pathological assessment of the TME specimen, including basic definitions, current international guidelines, an approach to evaluating the mesorectum and a discussion of special issues relating to margins, lymph node retrieval and effects of neoadjuvant therapy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".