Astigmatic Results of a Diffractive Trifocal Toric IOL Following Intraoperative Aberrometry Guidance
Bibliographic record
Abstract
John F Blaylock,1 Brad Hall2 1Valley Laser Eye Centre, Abbotsford, BC, Canada; 2Sengi, Penniac, NB, CanadaCorrespondence: Brad HallSengi, 473 Route 628, Penniac, NB E3A 8X8, CanadaTel +1 888-255-8680Email bhall@sengiclinical.comPurpose: To determine if intraoperative aberrometry (IA) improves astigmatic outcomes for trifocal toric IOL (TTI) cases.Patients and Methods: This was a retrospective study examining 137 eyes that underwent cataract extraction and TTI implantation using femtosecond laser, digital registration, and IA. Final cylinder power and axis of placement were determined by IA. Monocular uncorrected distance visual acuity (UDVA), uncorrected intermediate visual acuity (UIVA), uncorrected near visual acuity (UNVA), and refractive data were collected at 3 months. Postoperative residual astigmatism (PRA) determined by manifest refraction was compared to back-calculated residual astigmatism (BRA) using the cylinder power calculated preoperatively.Results: Postoperatively, 97.8% of eyes had IA PRA ≤ 0.50D and 80.3% had BRA ≤ 0.50 D, a difference of 17.5%. Mean PRA for IA was 0.07 D ± 0.19 (range 0.00– 1.00 D) compared to BRA 0.31 D ± 0.33 (range 0.00– 1.34 D) (P < 0.001). Cylinder power was changed in 50.4% of cases based upon IA. Postoperative mean UDVA (LogMAR) was 0.04 ± 0.09 (range − 0.12– 0.30 logMAR), and 65% of eyes were ≤ 0.0, 85% ≤ 0.1, and 99% ≤ 0.18.Conclusion: The proportion of eyes with PRA ≤ 0.50 D and mean PRA was significantly lower using IA versus the preoperative planned cylinder power.Keywords: PanOptix, trifocal IOL, toric IOL, cataract surgery
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".