Thrombotic events in metastatic colorectal cancer patients treated with leucovorin, fluorouracil and irinotican (FOLFIRI) plus bevacizumab.
Bibliographic record
Abstract
OBJECTIVE: To determine the incidence and risk factors for thrombotic events (TEs) in patients with metastatic colorectal cancer (mCRC) who received bevacizumab (BV) and FOLFIRI (leucovorin, fluorouracil and irinotican) compared to FOLFIRI alone. METHODS: Single institution retrospective study of 450 mCRC patients who received either BV plus FOLFIRI or FOLFIRI alone between April 2004 and August 2012. Demographics, TE risk factors, and treatment data were abstracted from patients' records. Multivariate analysis was used to identify factors that contributed to thromboembolism. RESULTS: Two-hundred-sixty-one mCRC patients received BV plus FOLFIRI [64.8% males, mean body mass index (BMI) of 27.6] compared to 189 control patients who received FOLFIRI alone (61.9% males, BMI 27.2). The incidence of TEs was 14.9% in the BV plus FOLFIRI group, compared to 15.9% in the control group. Multivariate analysis controlling for age, BMI, gender, malignancy, metastatic sites, line of treatment, and risk factors did not suggest a significant increase in the risk of TE with the addition of BV (OR =0.83 95% CI: 0.40-1.70; P=0.602). No difference in the site of TEs was observed between the treatment groups. The only statistically significant risk factor for thrombosis in the FOLFIRI plus BV group was increased BMI (OR =1.05; 95% CI: 1.01-1.10; P=0.01). CONCLUSIONS: This study does not support a significant increase in the risk of TE in patients with mCRC who received BV in addition to FOLFIRI. Increased BMI may be a risk factor for thrombosis in patients treated with BV.
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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 | 0.000 | 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".