The Current Burden and Future Solutions for Preoperative Cataract-Refractive Evaluation Diagnostic Devices: A Modified Delphi Study
Bibliographic record
Abstract
Purpose: To obtain consensus on the key areas of burden associated with existing devices and to understand the requirements for a comprehensive next-generation diagnostic device to be able to solve current challenges and provide more accurate prediction of intraocular lens (IOL) power and presbyopia correction IOL success. Patients and Methods: Thirteen expert refractive cataract surgeons including three steering committee (SC) members constituted the voting panel. Three rounds of voting included a Round 1 structured electronic questionnaire, Round 2 virtual face-to-face meeting, and Round 3 electronic questionnaire to obtain consensus on topics related to current limitations and future solutions for preoperative cataract-refractive diagnostic devices. Results: Forty statements reached consensus including current limitations (n = 17) and potential solutions (n = 23) associated with preoperative diagnostic devices. Consistent with existing evidence, the panel reported unmet needs in measurement accuracy and validation, IOL power prediction, workflow, training, and surgical planning. A device that facilitates more accurate corneal measurement, effective IOL power prediction formulas for atypical eyes, simplified staff training, and improved decision-making process for surgeons regarding IOL selection is expected to help alleviate current burdens. Conclusion: Using a modified Delphi process, consensus was achieved on key unmet needs of existing preoperative diagnostic devices and requirements for a comprehensive next-generation device to provide better objective and subjective outcomes for surgeons, technicians, and patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.120 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".