Use of the Delphi process in paediatric cataract management
Bibliographic record
Abstract
PURPOSE: To identify areas of consensus and disagreement in the management of paediatric cataract using a modified Delphi approach among individuals recognised for publishing in this field. DESIGN: A modified Delphi method. PARTICIPANTS: International paediatric cataract experts with a publishing record in paediatric cataract management. METHODS: The process consisted of three rounds of anonymous electronic questionnaires followed by a face-to-face meeting, followed by a fourth anonymous electronic questionnaire. The executive committee created questions to be used for the electronic questionnaires. Questions were designed to have unit-based, multiple choice or true-false answers. The questionnaire included issues related to the preoperative, intraoperative and postoperative management of paediatric cataract. MAIN OUTCOME MEASURE: Consensus based on 85% of panellists being in agreement for electronic questionnaires or 80% for the face-to-face meeting, and near consensus based on 70%. RESULTS: Sixteen of 22 invited paediatric cataract surgeons agreed to participate. We arrived at consensus or near consensus for 85/108 (78.7%) questions and non-consensus for the remaining 23 (21.3%) questions. CONCLUSIONS: Those questions where consensus was not reached highlight areas of either poor evidence or contradicting evidence, and may help investigators identify possible research questions.
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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 | 0.408 | 0.347 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".