Angle Kappa Influence on Multifocal IOL Outcomes
Bibliographic record
Abstract
Purpose: To characterize angle kappa and study the relationship between preoperative angle kappa and postoperative refractive accuracy, visual outcomes, and patient satisfaction in a large population of eyes with multifocal intraocular lens (MIOL) implantation. Methods: A comprehensive electronic medical record chart review of 26,470 consecutive eyes that underwent immediate sequential bilateral cataract or refractive lens exchange with MIOLs was conducted. The primary outcome measures were postoperative monocular uncorrected distance visual acuity (UDVA), manifest refraction sphere and cylinder, spherical equivalent (SEQ), defocus equivalent (DEQ), subjective quality of vision at near, intermediate, and distance, and the likelihood of recommending the procedure. Relationships between preoperative angle kappa and postoperative outcomes were assessed with Pearson correlations. Results: Angle kappa followed a right-skewed normal distribution ( R 2 = 0.99) with a mean ± standard deviation of 0.64 ± 0.27 mm. No clinically meaningful relationship was found between preoperative angle kappa and postoperative sphere, cylinder, SEQ, and DEQ, all with R 2 ⩽ 0.0005. Similarly, there was no clinically meaningful relationship between preoperative angle kappa and postoperative UDVA ( R 2 = 0.001), postoperative satisfaction for near, intermediate, and distance vision (all R 2 ⩽ 0.0023), or for recommending the MIOL surgery to friends and relatives ( R 2 = 0.0000). Conclusions: Preoperative angle kappa does not have a predictive clinical impact on postoperative MIOL visual outcomes, refractive accuracy, or subjective patient satisfaction. Angle kappa as a single variable cannot be used to determine MIOL candidacy. [ J Refract Surg . 2023;39(12):840–849.]
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".