Predictive Factors for Anastomotic Leakage Following Colorectal Cancer Surgery: Where Are We and Where Are We Going?
Bibliographic record
Abstract
Anastomotic leakage (AL) remains one of the most severe complications following colorectal cancer (CRC) surgery. Indeed, leaks that may occur after any type of intestinal anastomosis are commonly associated with a higher reoperation rate and an increased risk of postoperative morbidity and mortality. At first, our review aims to identify specific preoperative, intraoperative and perioperative factors that eventually lead to the development of anastomotic dehiscence based on the current literature. We will also investigate the role of several biomarkers in predicting the presence of ALs following colorectal surgery. Despite significant improvements in perioperative care, advances in surgical techniques, and a high index of suspicion of this complication, the incidence of AL remained stable during the last decades. Thus, gaining a better knowledge of the risk factors that influence the AL rates may help identify high-risk surgical patients requiring more intensive perioperative surveillance. Furthermore, prompt diagnosis of this severe complication may help improve patient survival. To date, several studies have identified predictive biomarkers of ALs, which are most commonly associated with the inflammatory response to colorectal surgery. Interestingly, early diagnosis and evaluation of the severity of this complication may offer a significant opportunity to guide clinical judgement and decision-making.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".