Tattooing or not? A review of current practice and outcomes for laparoscopic colonic resection following endoscopy at a tertiary care centre
Bibliographic record
Abstract
BACKGROUND: Because small colonic tumours may not be visualized or palpated during laparoscopy, location of the lesion must be identified before surgery. The aim of this study was to evaluate the effectiveness of the current recommendation of endoscopic tattooing of lesions prior to laparoscopic colonic resections. METHODS: All consecutive patients who underwent elective laparoscopic resection for a colonic lesion at a single tertiary institution between 2013 and 2015 were identified for chart review. RESULTS: In total, 224 patients underwent laparoscopic resection for a benign or malignant colonic lesion during the study period. All patients had a complete colonoscopy preoperatively. In all, 148 patients (66%) had their lesion tattooed at endoscopy. Most lesions were tattooed distally, but 15% were tattooed either proximally, both proximally and distally, or tattooed without specifying location as proximal or distal. Tattoo localization was accurate in 69% of cases. Tattooed lesions were not visible during surgery 21.5% of time; 2 cases were converted to open surgery to identify the lesion. Inaccuracy in endoscopic localization led to change in surgical plan in 16% of surgeries. In the nontattooed group, 1 case was converted to open surgery to localize the lesion, 3 required intraoperative colonoscopy and 1 had positive margins on final pathology. CONCLUSION: To improve surgical planning, we recommend the practice of endoscopic tattooing of all colon lesions at a location just distal to the lesion using multiple injections to cover the circumference of the bowel wall.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".