Digital eye strain and lens-based prescribing: exploring the gap between evidence and clinical practice
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.
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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 | 0.048 | 0.197 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".