Collagenase-producing bacteria are common in anastomotic leakage after colorectal surgery: a systematic review
Bibliographic record
Abstract
PURPOSE: Some gut bacteria can produce enzymes (collagenases) that can break down collagen in the intestinal wall. This could be a part of the pathophysiology of anastomotic leakage (AL). This systematic review aimed to investigate if such bacteria were present more frequently in AL patients versus non-AL patients following colorectal surgery. METHODS: , 2023, for randomized and observational human studies of AL following colorectal surgery with information on gastrointestinal bacteria. The primary outcome was bacteria with the potential to produce collagenase. The risk of bias was assessed with the Newcastle-Ottawa Scale, as all studies were observational. RESULTS: We included 15 studies, with a total of 52,945 patients, of which 1,747 had AL, and bacteriological information from feces, mucosa, the resected specimen, or drain fluid was presented. In 10 of the 15 studies, one or more collagenase-producing bacteria were identified in the patients with AL. Neither the bacteria nor the collagenase production were quantified in any of the studies. The studies varied greatly in terms of sample material, analytical method, and time of collection. Studies using DNA sequencing methods did not report findings of collagenase-producing bacteria. CONCLUSION: Collagenase-producing bacteria are more common in patients with AL following colorectal surgery than in patients without AL, but the significance is unclear. From the current studies, it is not possible to determine the pathogenicity of the individual gut bacteria.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".