Patient‐reported reasons for and predictors of noncompliance with compression stockings in a randomized trial of stockings to prevent postthrombotic syndrome
Bibliographic record
Abstract
INTRODUCTION: Elastic compression stockings (ECSs) are used to treat symptoms of venous insufficiency. However, lack of patient compliance can limit their effectiveness. In a secondary analysis of the SOX Trial, a randomized trial of active vs. placebo ECSs worn for 2 years to prevent postthrombotic syndrome after deep vein thrombosis, we aimed to describe patient-reported reasons for nondaily use of ECS and to identify predictors of noncompliance during follow-up. METHODS: At each follow-up visit of the SOX Trial, patients were asked how many days per week they wore study stockings, and if not worn daily, to specify the reason(s). Reasons for nondaily use of ECSs were tabulated. Multiple logistic regression modeling was used to identify predictors of stocking noncompliance during follow-up (defined as use <3 days per week). RESULTS: Among the 776 patients who attended at least 1 follow-up visit, daily use of stockings at each visit was similar in the active and placebo ECS groups. Reasons for nondaily use of stockings was most frequently related to aversive aspects of stockings (~three-fourths of patients) and less often related to patient behaviors (~one-fourth of patients). In multivariate analyses, behavior-related and aversive aspect-related reasons for nondaily use of ECSs at the 1-month visit were significant predictors of noncompliance during follow-up (odds ratio [OR] = 4.41 [95% confidence interval, 2.12-9.17] and OR = 3.99 [2.62-6.08], respectively). CONCLUSIONS: Aversive aspects of ECSs and patient behaviors are important reasons for noncompliance. Improving the appeal and tolerability of ECS and education directed at modifying patient behaviors may improve compliance.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".