Preoperative Chemotherapy Plus Bevacizumab and Morbidity after Resection of Colorectal Cancer Liver Metastases
Bibliographic record
Abstract
Aims and background: The addition of bevacizumab to preoperative chemotherapy is a common therapeutic practice in patients with colorectal liver metastases. The aim of the present study was to assess the effect of bevacizumab on postoperative complications after liver resection. Methods:A retrospective analysis was performed including patients who underwent liver resection for colorectal liver metastases after receiving chemotherapy with or without bevacizumab in two hospitals. Univariate logistic regression models were used to identify predictors of postoperative morbidity in both groups of patients. Results: A total of 76 patients were analyzed: 22 patients did not receive preoperative chemotherapy (control group), 21 patients received preoperative chemotherapy alone and 33 patients received preoperative chemotherapy in combination with bevacizumab. The median number of chemotherapy cycles received was 4 (range, 1-23) for the chemotherapy group and 7 (range, 2-36) for the chemotherapy plus bevacizumab group Morbidity rate was similar in the three groups of patients considered: 54.5 %, 47.6% and 39.4, respectively. The most common complications were infections and wound complications. The number of preoperative chemotherapy cycles received was the only clinical variable that was significantly correlated with postoperative comorbidity. Conclusions: Our results support the evidence that the addition of bevacizumab to preoperative chemotherapy does not increase the risk of complications following surgery of colorectal liver metastases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".