Avoiding the needle: A quality improvement program introducing apixaban for extended thromboprophylaxis after major gynecologic cancer surgery
Bibliographic record
Abstract
OBJECTIVE: Patients undergoing gynecologic cancer surgery at our centre are recommended up to 28 days of enoxaparin for extended post-operative thromboprophylaxis (EP). Baseline survey revealed 92% patient adherence, but highlighted negative effects on patient experience due to the injectable route of administration. We aimed to improve patient experience by reducing pain and bruising by 50%, increasing adherence by 5%, and reducing out-of-pocket cost after introducing apixaban as an oral alternative for EP. METHODS: In this interrupted time series quality improvement study, gynecologic cancer patients were offered a choice between apixaban (2.5 mg orally twice daily) or enoxaparin (40 mg subcutaneously once daily) at time of discharge. A multidisciplinary team informed project design, implementation, and evaluation. Process interventions included standardized orders, patient and care team education programs. Telephone survey at 1 and 6 weeks and chart audit informed outcome, process, and balancing measures. RESULTS: From August to October 2022, 127 consecutive patients were included. Apixaban was chosen by 84%. Survey response rate was 74%. Patients who chose apixaban reported significantly reduced pain, bruising, increased confidence with administration, and less negative impact of the medication (p < 0.0001 for all). Adherence was unchanged (92%). The proportion of patients paying less than $125 (apixaban cost threshold) increased from 45% to 91%. There was no difference in bleeding and no VTE events. CONCLUSIONS: Introduction of apixaban for EP was associated with significant improvement in patient-reported quality measures and reduced financial toxicity with no effect on adherence or balancing measures. Apixaban is the preferred anticoagulant for EP at our centre.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".