Adherence to apixaban for extended thromboprophylaxis after major abdominal or pelvic surgery for cancer: A prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Due to lack of data, direct oral anticoagulants are not considered by guidelines for venous thromboembolism (VTE) prophylaxis after cancer surgery. Adherence to low-molecular-weight heparin injections in this setting is sometimes poor. AIM: Analysis of adherence to oral apixaban for extended thromboprophylaxis. METHODS: Consecutive patients discharged after major surgery for abdominal/pelvic cancer and considered eligible for extended prophylaxis were offered apixaban 2.5 mg twice daily. Primary outcomes were adherence metrics-proportion of prescriptions filled, persistence (not prematurely discontinued), proportion of days covered (PDC) based on apixaban pill counts, and modified Morisky medication adherence scale at Days 28-30. Secondary outcomes were bleeding, VTE, and serious adverse events until Day 90. RESULTS: We included 53 patients, 51 were analyzed. Of 45 patients with prescriptions all had it filled (95% confidence interval [CI], 92%-100%). Persistence was 98% (95% CI, 90%-100%). PDC was ≥80% for 48 patients (94%; 95% CI, 84%-99%). We found good adherence (0/6 answers "yes") in 75% and moderate (1/6 answers "yes") in 25%. No major bleed or VTE occurred while on apixaban. CONCLUSION: Our results support good adherence with apixaban for VTE prophylaxis up to 28 days after major abdominal or pelvic cancer surgery.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".