Venous thrombosis in breast cancer patients receiving tamoxifen and/or chemotherapy: A systematic review
Bibliographic record
Abstract
6145 Background: The association between breast cancer and venous thromboembolism (VTE) is well established. It is also generally accepted that antineoplastic therapy contributes to VTE-risk. However, the contribution of specific treatments has not been precisely determined. Methods: We systematically reviewed the literature on the association between VTE and chemohormonal therapy for breast cancer. Two authors independently selected articles from the PUBMED database. Inclusion criteria were studies of (1) breast cancer patients, (2) antineoplastic therapy, (3) VTE as a primary outcome, and (4) English language. Exclusion criteria were studies with (1) a sample size of less than 20, or (2) no control group. A grading scale for article quality was developed with guidance from Levine M et al, JAMA: 271(20), 1994. Two investigators independently graded all eligible reports. Results: The search strategy yielded 460 articles. Eight articles met all inclusion and exclusion criteria. The kappa statistic for article selection revealed excellent agreement between the two abstractors (K=0.87). Three studies addressed the association of adjuvant tamoxifen with VTE, 3 addressed the association of combined tamoxifen and chemotherapy with VTE, and 2 addressed the association of chemotherapy alone with VTE. The studies were heterogeneous with respect to design and quality. Seven studies reported a positive association between tamoxifen and/or chemotherapy and VTE. No studies achieved the highest possible quality score on our scale. No studies reported that their experimental and control arms were balanced with respect to major VTE risk factors. Only 2 studies ensured that the diagnosis of VTE was objectively determined in all cases. Conclusions: Current data suggests that both chemotherapy and tamoxifen contribute to the risk of VTE in breast cancer patients. However, there are few studies investigating VTE as a primary endpoint in breast cancer treatment. Moreover, the quality of existing studies is not high. To determine how much breast cancer treatment contributes to VTE-risk additional trials with VTE as a pre-specified, primary outcome are required. No significant financial relationships to disclose.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".