The Current Burden and Future Solutions for Preoperative Cataract-Refractive Evaluation Diagnostic Devices: A Modified Delphi Study
Bibliographic record
Abstract
Bonnie An Henderson,1 Jaime Aramberri,2 Robin Vann,3 Adi Abulafia,4 Margaret Ainslie-Garcia,5 John Berdahl,6 Nicole Ferko,5 Kjell Gunnar Gundersen,7 So Goto,8,9 Preeya Gupta,10,11 Samuel Multack,12– 14 Elizabeth Persaud,5 Duna Raoof,15 Giacomo Savini,16 H John Shammas,17 Li Wang,18 Wendy Zhi Wang5 1Department of Ophthalmology, Tufts University School of Medicine, Boston, MA, USA; 2Ophthalmology Clinic Miranza Begitek, San Sebastian, Spain; 3Department of Ophthalmology, Duke University School of Medicine, Durham, NC, USA; 4Department of Ophthalmology, Shaare Zedek Medical Center, Hadassah Faculty of Medicine, the Hebrew University, Jerusalem, Israel; 5EVERSANA, Burlington, ON, Canada; 6Vance Thompson Vision, Sioux Falls, SD, USA; 7IFocus Øyeklinikk AS, Haugesund, Norway; 8Herbert Wertheim School Optometry and Vision Science, University of California, Berkeley, CA, USA; 9Department of Ophthalmology, National Hospital Organization, Tokyo Medical Center, Meguro-ku, Tokyo, Japan; 10Triangle Eye Consultants, Raleigh, NC, USA; 11Department of Ophthalmology, Tulane University, New Orleans, LA, USA; 12Laser and Cataract Institute, Frankfort, IL, USA; 13Advocate South Suburban Hospital, Hazel Crest, IL, USA; 14Advocate Trinity Hospital, Chicago, IL, USA; 15NVISION Eye Center, Newport Beach, CA, USA; 16IRCCS - G.B. Bietti Foundation, Rome, Italy; 17Department of Ophthalmology, University of Southern California, Los Angeles, CA, USA; 18Cullen Eye Institute, Department of Ophthalmology, Baylor College of Medicine, Houston, TX, USACorrespondence: Bonnie An Henderson, Tufts University School of Medicine, Boston, MA, USA, Tel +1 617 957 9279, Email bonnieanhenderson@gmail.comPurpose: 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.Keywords: next-generation diagnostic device, measurement accuracy and validation, IOL power prediction, surgical planning, cataract surgery
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".