Measuring Treatment Adherence in Myopia Control
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Topic: Treatment adherence is an essential consideration for both health providers and researchers evaluating the effectiveness of treatments of progressive childhood myopia. This narrative review provides an overview of methods used to measure treatment adherence and examines how adherence has been assessed in myopia control studies. Clinical Relevance: Despite its importance, adherence has not been consistently measured or reported in myopia control trials, limiting the reliability of conclusions regarding treatment efficacy, dose relationships, and safety. Examining current approaches and highlighting methodological trends and gaps will inform future research. Methods: Exploratory searches of literature were undertaken to identify relevant studies conducted between 2014 and 2024. Studies were included if they met the following criteria: (1) reported on treatment adherence outcomes, (2) involved pediatric populations, and (3) evaluated a myopia control intervention. Reference lists of included articles were scanned to identify additional relevant studies. Results: In the context of research in myopia control interventions, direct measures of adherence are often impractical and largely dependent on the intervention being examined. This has led to inconsistent reporting of adherence outcomes across studies. Consequently, most studies to date have relied on indirect methods, particularly self-reported data, because of the limited availability of reliable electronic monitoring tools and the inaccuracy or inappropriateness of dosage counts. Conclusions: It is recommended that researchers prioritize treatment adherence as a key outcome and select context-appropriate methods that minimize bias and error. Optimal measurement of adherence outcomes will support more robust analyses of treatment dose-response relationships and ultimately inform the clinical care of myopic patients. Financial Disclosures: The authors have no proprietary or commercial interest in any materials discussed in this article.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it