Platelet Activation and Platelet Indices as Markers for Disease Progression in Women with Breast Cancer
Bibliographic record
Abstract
Background: Several studies reported the role platelet activation, platelet volume (MPV) and other Indices in breast cancer but the data is inconsistent and/or diverse. The aim of this study was to systematically evaluate the role of platelet activation and platelet volume indices in women with BC as predictors for cancer progression and poor prognosis. Methods: Patients were recruited from our local oncology center between 2019 to 2020 following ethics approval. 80 patients with locally invasive BC, 20 metastatic and 100 controls were recruited. ADP-induced platelet activation was assessed by light-transmission aggregometry. Platelet P-selectin (CD62P) expression with and without ADP stimulation was assessed by flow cytometry. A comprehensive analysis of platelet count and platelet volume indices (PVIs) (MPV, PDW, MPV/P and PDW/P) was conducted. Data were analyzed in relation to tumor pathology, hormone receptors (ER, PR, HER-2) and proliferation index Ki-67. Regression analyses were conducted for the prediction of poor prognosis, tumor aggression and metastatic potential. Results: We found a significant increase in platelet aggregation (MA), CD62P expression, CD62P+ADP, MPV, PDW, MPV/P and PDW/P in the metastatic group compared to the locally invasive group. Univariate regression analysis showed significance for ADP, MA, CD62P+ADP, MPV and PDW/P. Conclusion: MPV/P and PDW/P can be used as simple low-cost predictors for cancer progression and poor prognosis. We conclude platelet activation and specific platelet indices can help predict prognosis in females with BC.
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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".