Comment on: “Angle Kappa is Not Correlated with Patient-Reported Outcomes After Multifocal Lens Implantation” [Letter]
Bibliographic record
Abstract
We read "Angle Kappa is Not Correlated with Patient-Reported Outcomes After Multifocal Lens Implantation" by Liu et al, which analyzes the impact of angle kappa on patient-reported outcomes after multifocal intraocular lens (MIOL) implantation. e noted the omission of a critical source reference that significantly contributes to the understanding of this debate and topic. In the Journal of Refractive Surgery Dec. 2023, we published "Angle Kappa Influence on Multifocal IOL Outcomes". This study examines MIOL patient-reported outcomes in relation to preoperative angle kappa in a cohort of 26,470 eyes. Their study, mirroring our conclusions, confirms that angle kappa lacks significant correlation with patient-reported outcomes and cannot be used as a criterion for MIOL candidacy. We demonstrated that preoperative angle kappa did not have a predictive clinical impact on postoperative MIOL visual outcomes, refractive accuracy, subjective patient satisfaction at near, intermediate and far distance, nor with the likelihood of recommending an MIOL procedure to friends and relatives. Our study also noted a larger angle kappa in right vs. left eyes. Liu et al add validity to our existing findings 2 and confirms, along with several previous studies, 3-9 that angle kappa as a single variable cannot be used to determine MIOL candidacy. The novelty of the study by Liu et al lies in reaching the same conclusion with angle kappa measurements taken by Pentacam and adding photic phenomena data.
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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 | 0.004 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.031 | 0.023 |
| Insufficient payload (model declined to judge) | 0.010 | 0.013 |
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".