ENHANCE—(Electronic Hydroxyurea Adherence): A Protocol to Increase Hydroxyurea Adherence in Patients with Sickle Cell Disease
Bibliographic record
Abstract
BACKGROUND: Hydroxyurea (HU) is the only disease-modifying medication for patients with sickle cell disease (SCD). HU can reduce SCD-related complications but only 35% to 50% of pediatric patients adhere to HU at the rates achieved in clinical trials and this limits its clinical effectiveness. Mobile Directly Observed Therapy (Mobile DOT) is a pilot-tested, electronic, multidimensional, HU adherence intervention that targets many components of the Health Behavior Model. OBJECTIVE: The aim of this study is to evaluate the impact of Mobile DOT on HU adherence in children with SCD. The objective of our study is to inform the development of future adherence interventions and pediatric SCD protocols. METHODS: This is a single-arm crossover study of pediatric patients with SCD. Participants self-record videos of their daily HU administrations and receive text message alerts to take HU, feedback on their HU adherence, and incentives when they achieve adherence goals during the 6-month Mobile DOT phase. Participants' HU adherence during the Mobile DOT phase is compared with their baseline HU adherence (6 months prior to study entry) and to their HU adherence 6 months after completing the Mobile DOT phase. The primary outcome of this study is HU adherence measured by medication possession ratio. RESULTS: The trial is ongoing. Preliminary review of participant satisfaction results suggest that most participants can complete Mobile DOT in less than 5 minutes per day and are satisfied with the intervention. CONCLUSIONS: If effective, the Mobile DOT strategy will increase HU adherence and this could improve patients' clinical outcomes and reduce costs of care.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.002 |
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".