An interplay between dermatology and ophthalmology: a systematic review and meta-analysis on intense pulsed light therapy for Meibomian gland dysfunction
Bibliographic record
Abstract
Objectives: To examine the effectiveness of intense pulsed light therapy (IPL) in treating Meibomian gland dysfunction (MGD) and dry eye symptoms. Methods: This study was conducted following the PRISMA statement guidelines. Literature sources included MEDLINE, Embase, Cochrane Library and meeting abstracts from COS and ARVO. Articles underwent 3 stages of screening before data extraction and meta-analysis. Results: 493 studies were found. 50 remained after title screening, 23 after abstract screening and 8 progressed to data extraction. Meta-analysis indicated a significant increase in tear break-up time (TBUT) post-IPL in the less than 1-month follow-up (SMD=1.45; CI:[0.33, 2.57]), 1.5–2 months follow-up (SMD=2.08; CI: [1.14, 3.01]), and 3-months follow-up (SMD=3.28; CI:[2.78, 3.78]) groups and a non-significant increase in TBUT in the 6-month follow-up (SMD=1.90; CI:[-0.18, 3.98]) and at 12-months follow-up (SMD=0.0; CI:[-0.48, 0.48]) groups. Meta-analysis also indicated a significant increase in Schirmer’s test values during the less than 1-month (SMD=0.91; CI:[0.50, 1.31]) and 6-month (SMD=0.65; CI:[0.25, 1.04]) follow-up periods and a non-significant increase in Schirmer’s test values during the 1.5–2 month follow-up period (SMD=0.41; CI:[-0.93, 1.75]). Conclusions: The results suggested a significant increase up to 5-months and a non-significant increase at 6-months post-IPL for TBUT. They also suggested a significant increase in Schirmer’s test values during the less than 1-month and 6-month follow-up periods and a non-significant increase in Schirmer’s test values during the 1-month follow-up period. Ultimately, IPL seems to be a promising therapy for MGD, but we recommend future studies with longer follow-up periods.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.038 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".