The Role of transanal (Ta) dissection in the management of difficult primary and recurrent rectal cancer
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to review the postoperative and short-term oncological outcomes of our first cohort of patients having had a transanal (Ta) approach for primary or recurrent rectal cancer. METHODS: A retrospective chart review was performed on all cases of Ta dissection occurring between 2013 and 2016. We reviewed data concerning case selection, tumour characteristics, perioperative and postoperative data and final pathology. RESULTS: A total of 24 males were operated for primary (92% (22/24)) or recurrent rectal cancer (8.3% (2/24)). Four patients (16.7% (4/24)) had a history of previous rectal surgery and two had a history of previous Ta total mesorectal excision (TME). A majority of patients were obese, with 58.3% (14/24) having a body mass index >30. The laparoscopic approach was used in the majority of cases (95.8% (23/24)). Most patients had a low anterior resection (95.8% (23/24)). Sixteen patients received a temporary ileostomy (66.7% (16/24)). Three patients suffered perioperative complications (including colonic ischaemia, rectal perforation and arterial bleeding). Five patients (21.7% (5/23)) had an anastomotic leak treated with Ta drainage in two patients. Final pathology revealed negative margins in 95.8% (23/24). TME was considered complete in 87.5% (21/24) overall and in 95% (21/22) when considering only primary cancer cases. CONCLUSION: According to our cohort of selected difficult cases, Ta dissection approach helped achieve complete mesorectal excision in complex primary rectal cancer but also allowed for rectal resection in patients with previous rectal surgery. This technique also helped perform a primary anastomosis in these difficult cases.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".