Efficacy of treatments for Demodex blepharitis: A systematic review and meta-analysis
Bibliographic record
Abstract
PURPOSE: We conducted a systematic review and meta-analysis to evaluate the efficacy of different treatment for Demodex blepharitis. Parameters studied were mites count, improvement of symptoms and mites' eradication, stratified on type of treatments and mode of delivery of treatments (local or systemic). METHOD: The PubMed, Cochrane Library, Embase, ClinicalTrials.gov, Google scholar and Science Direct databases were searched for studies reporting an efficacy of treatments for Demodex blepharitis. RESULTS: We included 19 studies (14 observational and 5 randomized clinical trials), for a total of 934 patients, 1741 eyes, and 13 different treatments. For mites count, eradication rate, and symptoms improvement, meta-analysis included fifteen, fourteen and thirteen studies, respectively. The overall effect sizes for efficiency of all treatments, globally, were 1.68 (95CI 1.25 to 2.12), 0.45 (0.26-0.64), and 0.76 (0.59-0.90), respectively. Except usual lid hygiene for mites count, Children's Hospital of Eastern Ontario ointment (CHEO) for both eradication rate and symptoms, and CHEO, 2% metronidazole ointment, and systemic metronidazole for eradication rate, all treatments were efficient. Stratified meta-analysis did not show significant differences between local and systemic treatments (1.22, 0.83 to 1.60 vs 2.24, 1.30 to 3.18 for mites count; 0.37, 0.21 to 0.54 vs 0.56, 0.06 to 0.99 for eradication rate; and 0.77, 0.58 to 0.92 vs 0.67, 0.25 to 0.98 for symptoms improvement). CONCLUSION: We reported the efficiency of the different treatments of Demodex blepharitis. Because of less systemic side effects, local treatments seem promising molecules in the treatment of Demodex blepharitis.
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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 | 0.020 | 0.036 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.030 | 0.050 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".