Delphi-Based Global Consensus on Adopting Endothelial Keratoplasty: An Endothelial Keratoplasty Learners Group Initiative
Bibliographic record
Abstract
PURPOSE: To identify areas of consensus among experts on the performance of endothelial keratoplasty by using a modified Delphi approach, to help create a framework for novice surgeons to adopt these procedures. METHODS: Thirty-one international experts in endothelial keratoplasty participated. Two rounds of electronic survey were followed by a hybrid, virtual meeting. Consensus was set at 75%, and results with agreement between 70% and 75% were deemed as achieving near consensus. RESULTS: A consensus was reached for 98 statements covering the preoperative, intraoperative, and postoperative aspects of Descemet membrane endothelial keratoplasty (DMEK) and Descemet stripping endothelial keratoplasty/Descemet stripping automated endothelial keratoplasty. Four statements achieved near consensus, and consensus could not be achieved for 11 statements. For DMEK, the panel supported a peel technique to prepare tissue for endothelium out DMEK, implanted via an injector and supported by a near full air/gas fill as a baseline procedure onto which more advanced techniques can be built. DMEK tissue should be marked to ensure correct orientation. An inferior peripheral iridotomy should be used to prevent pupil block when a near full air/gas fill in used in endothelial keratoplasty (EK). Descemet stripping automated endothelial keratoplasty was considered preferable to Descemet stripping endothelial keratoplasty where access to microkeratome preparation was available. CONCLUSIONS: The Delphi process allowed areas of consensus on the performance of EK to be established by a group of international experts. The statements generated are a helpful framework for novice surgeons learning EK. Further research is needed to help determine what specific tomographic features indicate EK, when guttae are considered visually significant and how to approach combined aphakia and endothelial dysfunction.
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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 | 0.147 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".