MACULAR HOLE HYDRODISSECTION TECHNIQUE WITH HUMAN AMNIOTIC MEMBRANE FOR REPAIR OF LARGE MACULAR HOLES
Bibliographic record
Abstract
PURPOSE: To describe a combined surgical technique using the macular hole hydrodissection (MHH) with human amniotic membrane for repair of large macular holes. METHODS: A step-by-step procedure and a surgical video using the combined MHH and human amniotic membrane technique are presented. DESCRIPTION AND TECHNIQUE: As the first step, the MHH separates the adhesions of the macular hole to the underlying retinal pigment epithelium with a soft-tipped cannula through proportional reflux followed by gentle passive aspiration. The human amniotic membrane graft is marked to identify the nonsticky epithelial side and ensure that the stromal layer (sticky and nonshinny) is facing downward toward the retinal pigment epithelium. The graft is then tucked into the space created with MHH between the macular hole edges and the retinal pigment epithelium with closed forceps to decrease the likelihood of the graft from dislocating postoperatively. CONCLUSION: The MHH in combination with the human amniotic membrane is a practical and effective technique for addressing challenging large macular holes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".