Chemotherapy-Related Thrombocytosis: Does It Increase the Risk of Thromboembolism?
Bibliographic record
Abstract
OBJECTIVES: Chemotherapy increases the risk of thromboembolism in patients with cancer. Although thrombocytopenia is a known side effect of chemotherapy, reactive thrombocytosis related to chemotherapy is uncommonly reported. The present study aimed to determine the incidence of gemcitabine-related thrombocytosis and the associated risk of thromboembolism. METHODS: Medical records of 250 consecutive patients with a malignant disease who received gemcitabine-based therapy were reviewed. A multivariate analysis was done to determine factors associated with thromboembolism. RESULTS: A total of 220 eligible patients with a median age of 63 years (range 26-83) were identified. Of these 220 patients, 95% had advanced malignancy and 59% had received prior chemotherapy. A total of 69% of patients received a platinum combination. In all, 46% patients experienced thrombocytosis following chemotherapy, with a median platelet count of 632 × 10(9)/l (range 457-1,385). Twenty-three of the 220 patients experienced a vascular event within 6 weeks of treatment. Eleven patients with thrombocytosis experienced a vascular event compared with 10 patients without thrombocytosis (not significant). On multivariate analysis, leukocytosis (odds ratio 5.8, 95% confidence interval 2.1-15.8) and comorbid illnesses (odds ratio 4.1, 95% confidence interval 1.4-12.6) were correlated with thromboembolism. CONCLUSIONS: Although gemcitabine-based therapy has been associated with an increased incidence of thrombocytosis, it does not increase the risk of thromboembolism in cancer patients. Leukocytosis and comorbid illnesses do increase the risk of thromboembolism.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".