Dry Eye Disease Management Via Technological Methods: A Systematic Review and Network Meta-analysis
Bibliographic record
Abstract
INTRODUCTION: In recent years, various technological therapeutic modalities have emerged aiming to target the underlying pathophysiology of dry eye disease (DED). METHODS: A systematic search was conducted in PubMed, Scopus, and Embase databases up to July 29, 2023, using predefined search terms related to DED and technological treatments, including intense pulsed light (IPL), LipiFlow, TearCare, iLux, low-level light therapy (LLLT), and acupuncture. Randomized controlled trials (RCTs) evaluating technological interventions for DED with outcome measures for tear secretion, meibomian gland quality, tear break-up time (TBUT), corneal surface health, and symptom scores at 1-2 months post-treatment were included. Data extraction followed PRISMA guidelines. Risk of bias was assessed using Cochrane guidelines. A random-effects frequentist network meta-analysis model was employed, and standardized mean differences (SMDs) were calculated for comparative analyses. P-scores were used to rank treatment efficacy. RESULTS: Ultimately, 45 RCTs involving 3455 patients were included. TearCare combined with meibomian gland expression (MGX) demonstrated the highest efficacy for improving meibomian gland secretion (SMD - 10.08, 95% CI - 13.35 to - 6.82). IPL-based treatments, including IPL combined with diquafosol sodium or LLLT, significantly improved TBUT and symptom scores, with IPL alone ranking highest for symptom relief (P-score 0.811). Acupuncture was the only intervention significantly superior to conservative treatment for increasing Schirmer test values (SMD - 0.69, 95% CI - 1.06 to - 0.32). LipiFlow demonstrated modest improvements but was not significantly superior to other technologies. CONCLUSIONS: These findings underscore the potential of advanced technological interventions in the short-term management of DED and support the need for standardized, long-term comparative studies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".