Antiangiogenic Drug-Induced Proteinuria as a Prognostic Factor in Metastatic Colorectal Cancer
Bibliographic record
Abstract
Treatment with bevacizumab is known to cause adverse events such as proteinuria and hypertension, amongst others. However, while bevacizumab-induced hypertension has been linked to increased overall survival (OS), data on proteinuria are controversial. We performed a retrospective analysis to observe the influence of adverse events developed during treatment with bevacizumab and chemotherapy on the OS in patients with metastatic colorectal cancer (mCRC). Kaplan–Meier and log-rank analyses were used to assess differences in OS, and hazard ratios (HR) were estimated using Cox models. Out of the 3497 mCRC patients admitted to our center between 2014 and 2019, 150 met the criteria for inclusion in our analysis. Out of these, 50.7% experienced proteinuria and had reached a longer OS (40 versus 25 months, p = 0.015) and progression-free survival (15 versus 12 months, p = 0.039). The following groups were identified as having a lower risk of death: patients with proteinuria (HR 0.589; 95% CI 0.402–0.863; p = 0.007), one metastatic site (HR 0.533; 95% CI 0.363–0.783; p = 0.001), and non-metastatic stage at diagnosis (HR 0.459; 95% CI 0.293–0.720; p = 0.001). Patients with anemia and diabetes had an increased risk of death. Proteinuria emerges as a useful prognostic factor in mCRC patients undergoing bevacizumab-based systemic therapy, and it could be easily integrated into the decision-making process, thus allowing physicians to further individualize systemic treatments.
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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 | 0.003 |
| 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".