CTRH as Biomarker of Drug Efficacy: In-Depth Assessment of Real-World Evidence
Bibliographic record
Abstract
BACKGROUND: Observational studies have suggested that cancer treatment-related hypertension (CTRH) is associated with improved survival and could possibly serve as a biomarker of drug efficacy. Our review aimed to provide an in-depth assessment of the methodological quality of available observational studies. METHODS: We systematically searched MEDLINE/PubMed from inception to January 2025 for observational studies that assessed the potential association between the development of CTRH and the risk of cancer-related outcomes, including progression-free survival and overall survival. We assessed the methodological quality of the identified studies using the Risk of Bias in Nonrandomized Studies of Interventions tool. RESULTS: We identified 25 observational studies with a total of 6364 patients treated for different cancer types that assessed the potential association between CTRH and the risk of progression-free survival and overall survival. All studies examined CTRH related to the use of vascular endothelial growth factor inhibitors. CTRH was mostly associated with improved progression-free survival and overall survival across cancer types with up to 79% decreased risks. Based on the Risk of Bias in Nonrandomized Studies of Interventions, 8 studies were at critical, 13 studies were at serious, and 4 studies were at moderate risk of bias. Major biases included important residual confounding, reverse causality, immortal time bias, and exposure misclassification. In studies at moderate risk of bias, the survival benefits associated with CTRH disappeared or were attenuated significantly. CONCLUSIONS: Observational studies alluding to CTRH being a marker of drug efficacy have major, potentially conclusion-altering biases. Therefore, the findings of our review do not support CTRH as a biomarker of drug efficacy.
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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 | 0.178 | 0.453 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".