Intra‐operative transanal near infrared imaging of colorectal anastomotic perfusion: a feasibility study
Bibliographic record
Abstract
AIM: Anastomotic dehiscence is a devastating complication. Inadequate blood supply is felt to be the prevailing cause. This study describes the use of near infrared imaging to evaluate transanally anastomotic tissue perfusion following low anterior resection. METHOD: Twenty patients undergoing low anterior resection for benign and malignant disease were studied. After completing the anastomosis, indocyanine green (ICG) was injected via a peripheral intravenous catheter. An endoscopic near infrared imaging system (Pinpoint, Novadaq, Canada) was then used transanally to visualize mucosal perfusion of the colon, rectum and the anastomotic staple line. RESULTS: All patients underwent a technically successful ICG angiogram. The angiogram was abnormal in four patients. Two of these had a protective loop ileostomy and showed no sign of anastomotic breakdown. The other two patients were found on CT scan to have a peri-anastomotic collection consistent with anastomotic leakage. Both were managed conservatively with resolution. CONCLUSION: This study confirms that transanal ICG angiography is feasible and provides imaging of mucosal and anastomotic blood flow. The technique warrants further study in a larger group of patients to assess its ability to identify defects in tissue perfusion that may lead to anastomotic breakdown.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".