Refractive Outcomes Following Trifocal Intraocular Lens Implantation in Post-Myopic LASIK and PRK Eyes
Bibliographic record
Abstract
John F Blaylock,1 Brad J Hall2 1Medical Director/Chief Surgeon, Valley Laser Eye Centre, Abbotsford, BC, Canada; 2Clinical Research Consultant, Sengi, Penniac, NB, CanadaCorrespondence: Brad J Hall, Sengi, 473 Route 628, Penniac, NB, E3A8X8, Canada, Tel +1 888.255.8680, Email bhall@sengiclinical.comPurpose: To assess refractive outcomes of a trifocal intraocular lens (IOL) in post-myopic laser refractive surgery eyes.Methods: This was a retrospective chart review of 35 eyes (21 patients), with history of laser refractive surgery, who were implanted with a trifocal IOL. Surgeon’s standard procedure included femtosecond laser (FLACS), digital registration, and intraoperative aberrometry (IA). The primary outcome measure was absolute prediction error. Secondary measures were refractive outcomes, postoperative residual astigmatism (PRA), monocular uncorrected visual acuity at distance (UDVA; 4m), intermediate (UIVA; 60cm), and near (UNVA; 40cm), and monocular best-corrected visual acuity at distance (BCVA; 4m).Results: At 3 months postoperatively, 71% and 68% of eyes had absolute prediction error 0.5 D or less with IA and preoperative planning respectively, which was not statistically significant (p > 0.05). The PRA was 0.5 D or less in 91% of eyes with IA and 56% of eyes with preoperative planning. The PRA differences between IA and preoperative planning were statistically significant (p < 0.002). The percentage of eyes 20/20 or better for monocular UCVA, BCVA, UIVA, and UNVA was 29%, 77%, 78%, and 66%, respectively. Absolute prediction error 0.5 D or less was significantly higher in post-LASIK eyes versus post-PRK eyes (p < 0.003), at 85% and 56% of eyes, respectively.Conclusion: Implantation with a trifocal IOL can provide acceptable refractive and visual outcomes with minimal residual astigmatism in post-myopic LASIK and PRK eyes.Keywords: PanOptix IOL, cataract surgery, intraoperative aberrometry, post-refractive
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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 | 0.001 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".