Digital eye strain and lens-based prescribing: exploring the gap between evidence and clinical practice
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
In response to the growing incidence of digital eye strain, a variety of spectacle and contact lens interventions have been introduced and are frequently prescribed in clinical and retail settings. However, the evidence supporting their effectiveness remains limited and inconclusive. This narrative review explores the real-world implementation of lens-based interventions for digital eye strain, focusing on how contextual factors influence prescribing practices. Using the Consolidated Framework for Implementation Research, the review examines the characteristics of these interventions, the outer setting in which they are prescribed, individuals involved in their adoption, and the processes that support or hinder their integration into routine care. Findings reveal that prescribing is often driven more by societal demand, commercial pressures, and clinician perceptions than by robust clinical evidence. Blue light filtering and anti-reflective coated spectacle lenses are commonly recommended, while contact lens interventions are less frequently studied but increasingly marketed. The review highlights a disconnect between evidence and practice and underscores the need for more rigorous research and context-specific clinical guidance to support evidence-based prescribing for digital eye strain.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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