The effectiveness of ocriplasmin versus surgery for the treatment of macular holes: A systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To conduct a systematic review looking at the effects of ocriplasmin compared to pars plana vitrectomy on macular holes to assess the effectiveness of the treatment options. METHODS: Literature was searched through MEDLINE, EMBASE, CINAHL, Clinical Trials.gov, and ProQuest Dissertations and Theses until June 12, 2018. Conferences held through Association for Research in Vision and Ophthalmology, Canadian Society of Ophthalmology, and American Academy of Ophthalmology were searched until June 18, 2018. A total of 208 records were screened leaving 26. One author independently reviewed them for quality and extracted data. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines were followed. The adverse events, MH closure rate, change in MH size, and the extent to which the patients' visual acuity is restored by each treatment option; ocriplasmin and vitrectomy. RESULTS: Twenty-six articles were included for qualitative and quantitative analysis. Meta-analysis results showed a 34% closure of macular holes after ocriplasmin treatment compared to 92% after vitrectomy. A significant improvement in visual acuity was seen after vitrectomy (SMD = -1.42; CI: [-1.98, -0.86]) as well as the ocriplasmin treatment (SMD = -0.73; CI: [-0.98, -0.48]). CONCLUSIONS: Results suggested 92% macular hole closure after vitrectomy compared to 34% after ocriplasmin. A significant improvement in visual acuity of patients was seen after both treatments. More good quality randomized controlled trials are required to make strong conclusions.
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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 | 0.017 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.037 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".