A quantitative synthesis of VR-based treatments for convergence insufficiency: A systematic review
Bibliographic record
Abstract
Purpose: Convergence insufficiency (CI), a common binocular vision disorder, impairs near eye alignment, causing eyestrain, headaches, and blurred vision. This systematic review aims to synthesize quantitative evidence on the efficacy of virtual reality (VR)-based treatments for CI, comparing their effectiveness to traditional therapies (e.g., pencil push-ups, office-based vision therapy) and assessing clinical outcomes and patient engagement. Methods: Following PRISMA 2020 guidelines, we searched PubMed, Scopus, and Web of Science (2000–2025) for randomized controlled trials (RCTs) and observational studies on VR-based interventions for CI. Inclusion criteria included CI diagnosis, VR interventions, and quantitative outcomes (near point of convergence [NPC], positive fusional vergence [PFV], Convergence Insufficiency Symptom Survey [CISS] scores). Study quality was evaluated using the Cochrane risk of bias tool and Newcastle-Ottawa Scale. Meta-analyses employed random-effects models, with heterogeneity (I²) and publication bias (Egger’s test) assessed. Results: From 342 articles, 12 studies (7 RCTs, 5 observational, n=589, ages 7–35) were included. VR interventions (headsets, anaglyph systems, gamified platforms) yielded moderate effect sizes (SMD=0.48–0.65), with NPC reductions of 2.5–4.8 cm, PFV increases of 8–12 prism diopters, and CISS score reductions of 10–15 points, outperforming traditional therapies. Compliance was higher (80–95%) with VR due to immersive engagement. Moderate heterogeneity (I²=45–60%) and minimal publication bias (p>0.05) were observed. Conclusion: VR-based treatments are promising for CI, offering enhanced outcomes and compliance. Larger, standardized trials are needed to confirm efficacy and address accessibility.
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How this classification was reachedexpand
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".