Colonic perforation with intraluminal stents and bevacizumab in advanced colorectal cancer: retrospective case series and literature review
Bibliographic record
Abstract
BACKGROUND: Self-expanding metal stents (SEMS) are increasingly used in the treatment of malignant large bowel obstruction in the setting of inoperable colorectal cancer. Perforation is a well-known complication associated with these devices. The addition of the vascular endothelial growth factor inhibitor bevacizumab is suspected to increase the rate, but the extent of the increase is not known. METHODS: We retrospectively reviewed the records of patients receiving SEMS in tertiary hospitals in Calgary, Alta., between October 2001 and January 2012. RESULTS: We reviewed the records of 87 patients with inoperable colorectal cancer who received SEMS during our study period. Nine perforations occurred in total: 4 of 30 (13%) patients who received no chemotherapy, 3 of 47 (6%) who received chemotherapy but no bevacizumab, and 2 of 10 (20%) who received chemotherapy and bevacizumab. These two patients received bevacizumab with FOLFIRI after SEMS placement, and they had peritoneal disease. CONCLUSION: Our case series and other studies suggest that bevacizumab may increase the risk of colonic perforation in the setting of SEMS. Caution should be used when combining these therapies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".