Does the presence of an intact primary increase the risk of nonelective colorectal surgery in patients treated with bevacizumab?
Bibliographic record
Abstract
AIM: In patients with incurable metastatic colorectal cancer (mCRC), resection of the primary tumour is debated; however, patients with intact primaries may be at a higher risk of complications requiring surgery when receiving treatment with bevacizumab. Our aim was to estimate the risk of nonelective colorectal surgery in patients undergoing bevacizumab therapy for mCRC and evaluate the association between intact primary tumours and risk of nonelective surgery. METHOD: We designed a population-based, retrospective cohort study using administrative and cancer registry data in Ontario, Canada. We included patients with mCRC who received bevacizumab from 1 January 2008 to 31 December 2014. The primary outcome was nonelective colorectal surgery after initiation of bevacizumab. We determined the cumulative incidence of nonelective colorectal surgery among patients with previously resected and unresected primaries, accounting for the competing risk of death. We explored the relationship between previous resection of the primary and need for nonelective surgery using a cause-specific hazards model, controlling for patient, tumour and treatment factors. RESULTS: We identified 1840 (32.7%) patients with intact primaries and 3784 (67.3%) patients with prior resection. The cumulative incidence of nonelective surgery 1 year after initiating bevacizumab for all patients was 3.9% (95% CI 3.4-4.5%). One-year cumulative incidence was higher in those with intact primaries than in those with resected primaries (6.1% vs 2.9%, P < 0.0001). After adjustment, an intact primary remained strongly associated with nonelective colorectal surgery (hazard ratio = 2.89, 95% CI 2.32-3.61; P < 0.0001). CONCLUSION: Bevacizumab is associated with a low but meaningful risk for serious gastrointestinal complications, necessitating vigilance, particularly among patients with an intact primary tumour.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".