Thrombotic events in metastatic colorectal cancer patients treated with FOLFIRI plus bevacizumab.
Bibliographic record
Abstract
e14630 Background: To determine the incidence and risk factors for thrombotic events (TEs) (arterial and venous) in patients with metastatic colorectal cancer (mCRC) who received Bevacizumab and FOLFIRI (Leucovorin, Fluorouracil and Irinotican ) compared to FOLFIRI alone. Methods: Single institution retrospective study of 450 mCRC patients who received either Bevacizumab plus FOLFIRI or FOLFIRI alone between October 2006 and September 2012. Demographics , TE risk factors and treatment data were abstracted from patients records. Multivariate analysis was used to determine factors that contributed to increased TE incidence. Results: 261 mCRC patients received Bevacizumab plus FOLFIRI ( 64.8 % males , mean Body Mass Index (BMI) 26.1 ) compared to 189 control patients who received FOLFIRI alone ( 61.1 % males ,BMI 27). The incidence of TEs was 15 % (arterial 1.8% + venous 13.2%) in the Bevacizumab plus FOLFIRI group compared to 15.8% (arterial 2.1% + venous 13.7%) in the control groups. Multivariate analysis controlled for age, BMI, gender, malignancy, metastatic sites , line of treatment, and risk factors did not suggest a significant increase in risk of TE associated with Bevacizumab (OR=0.83 95% CI: 0.40 - 1.70; p=0.602). No difference in locations of TEs was observed between both groups . The only statistically significant factor for thrombosis in Bevacizumab group was increased BMI (OR=1.05; 95% CI: 1.01- 1.10; p=0.016). Conclusions: Our data suggest that bevacizumab did not significantly increase the rate of thrombosis in patients with mCRC when added to FOLFIRI. To our knowledge this is the first study reporting the rate of TEs (arterial and venous) in this population.Our data suggest that BMI may be a risk factor for increase risk of thrombosis in patients treated with bevacizumab . Clinician should consider risk factors assessment prior to initiating bevacizumab .
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".