Systemic antibiotic treatment for meibomian gland dysfunction—A systematic review and meta‐analysis
Bibliographic record
Abstract
PURPOSE: To review the efficacy and safety of oral doxycycline antibiotics versus macrolides in the treatment of meibomian gland dysfunction (MGD). DESIGN: Systematic review and meta-analysis. METHODS: We performed a systematic search of electronic databases for all peer-reviewed published studies which included clinical outcomes of oral antibiotic MGD treatment. Individual study data were extracted and evaluated in a weighted pooled analysis, including total sign and symptom scores, meibomian gland secretion score, tear break-up time (TBUT), fluorescein staining score and rate of complications. RESULTS: Two thousand nine hundred and thirty-three studies were found, of which 54 were eligible for the systematic review, and six prospective studies were ultimately included for analysis, reporting on 563 cases from three countries. Age of affected patients ranged between 12 and 90 years. Overall, both treatment methods induced improvement in MGD signs and symptoms. In pooled analysis, macrolides were significantly superior in the total signs score (pooled standardized mean difference (SMD) -0.51, 95% confidence interval (CI): -0.99 to -0.03), meibomian gland secretion score (pooled SMD -0.25, 95%CI: [-0.48, -0.03]), TBUT (SMD -0.31, 95%CI: [-0.50, -0.13]) and fluorescein staining score (SMD -1.01, 95%CI: [-1.72, -0.29]). Moreover, while no severe complications were reported for both treatments, the macrolide group exhibited significantly less adverse events (pooled odds ratio 0.24 with a 95% CI of 0.16 to 0.34). CONCLUSIONS: Both macrolides and tetracyclines are effective treatments for MGD. In this study, macrolides exhibited better efficacy and safety profile compared to tetracyclines.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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".