The role of neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio as venous thromboembolism predictors in breast cancer patients pre- and post-therapy
Bibliographic record
Abstract
OBJECTIVES: Breast cancer (BC) accounts for 12.3% of all cancer-associated venous thromboembolism (VTE). Platelet-to-lymphocyte ratio (PLR) and neutrophil-to-lymphocyte ratio (NLR) are recognized inflammatory biomarkers but have not been incorporated into thrombosis risk stratification models. We evaluated NLR and PLR as predictive biomarkers for VTE in BC patients to determine their optimal predictive cutoffs and net predictive value before and after treatment. METHODS: We conducted a prospective pilot study that involved 56 women with BC, recruited prior to treatment (chemotherapy and immunotherapy) initiation with at least 6-month monitoring for VTE. NLR and PLR were assessed pre and posttreatment. RESULTS: Five patients (8.9%) developed VTE. NLR and PLR increased significantly posttreatment (P = 0.001). Post, not pretreatment, NLR (P = 0.029) and PLR (P = 0.033) were significantly associated with VTE occurrence. Receiver Operating curve analysis indicated enhanced predictive capacity for VTE postimmunotherapy. Optimal posttreatment cutoffs were 3.6 for NLR and 280 for PLR, aligning with existing literature, with slightly elevated NLR. CONCLUSIONS: Posttreatment NLR and PLR have higher predictability for VTE in patients receiving immunotherapy compared to chemotherapy. NLR outperforms PLR, particularly postimmunotherapy. This data holds promise for thrombosis risk stratification in the context of immunotherapy but requires evaluation in larger studies.
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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 | 0.001 |
| 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) | 0.000 | 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".