Patient preferences for venous thromboembolism prophylaxis after injury: a discrete choice experiment
Bibliographic record
Abstract
Objective Limited evidence for the optimal venous thromboembolism (VTE) prophylaxis regimen in orthopaedic trauma leads to variability in regimens. We sought to delineate patient preferences towards cost, complication profile, and administration route (oral tablet vs. subcutaneous injection). Design Discrete choice experiment (DCE). Setting Level 1 trauma center in Baltimore, USA. Participants 232 adult trauma patients (mean age 47.9 years) with pelvic or acetabular fractures or operative extremity fractures. Primary and secondary outcome measures Relative preferences and trade-off estimates for a 1% reduction in complications were estimated using multinomial logit modelling. Interaction terms were added to the model to assess heterogeneity in preferences. Results Patients preferred oral tablets over subcutaneous injections (marginal utility, 0.16; 95% CI: 0.11 - 0.21, P <0.0001). Preferences changed in favor of subcutaneous injections with an absolute risk reduction of 6.98% in bleeding, 4.53% in wound complications requiring reoperation, 1.27% in VTE, and 0.07% in death from pulmonary embolism (PE). Patient characteristics (sex, race, type of injury, time since injury) affected patient preferences ( P <0.01). Conclusions Patients preferred oral prophylaxis and were most concerned about risk of death from PE. Furthermore, the findings estimated the trade-offs acceptable to patients and heterogeneity in preferences for VTE prophylaxis.
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".