Clinical Outcomes of Monofocal Toric IOLs Using Digital Tracking and Intraoperative Aberrometry
Bibliographic record
Abstract
John F Blaylock,1 Brad J Hall2 1Valley Laser Eye Centre, Abbotsford, BC, V2S 3R1, Canada; 2Sengi, Penniac, NB, E3A 8X8, CanadaCorrespondence: Brad J HallSengi, 473 Route 628, Penniac, NB, E3A8X8, CanadaTel +1 888 255 8680Email bhall@sengiclinical.comPurpose: To evaluate clinical outcomes of a toric IOL using digital tracking (DT) and intraoperative aberrometry (IA).Methods: This was a retrospective, single surgeon study examining 151 eyes of 106 patients. Inclusion criteria were subjects who presented with visually significant cataracts (or as a candidate for clear lens extraction) and were implanted with a toric intraocular lens. Spherical equivalent prediction errors for IA and preoperative planning were calculated and compared. Preoperative and postoperative refractive data and monocular uncorrected distance visual acuity (UDVA) and corrected distance visual acuity (CDVA) were also collected at 3 months postoperatively.Results: Postoperative actual residual refractive astigmatism with IA was 0.50 D or less in 140 eyes (92.8%) and was 0.50 D or less in 88 eyes (58.3%) with back-calculations based on preoperative planning. The absolute spherical equivalent prediction error was 0.50 D or less in 135 eyes (89.4%) for IA compared to 123 eyes (85.4%) for preoperative planning. Postoperative monocular UDVA was 0.10 logMAR or better in 124 eyes (82.1%) and 0.00 logMAR or better in 90 eyes (59.6%). Postoperative CDVA was 0.10 logMAR or better in 147 eyes (97.4%) and 134 eyes (88.7%) were 0.00 logMAR or better.Conclusion: The results demonstrate that toric implantation with DT and IA can provide excellent refractive and visual outcomes.Keywords: toric IOL, cataract surgery, intraoperative aberrometry
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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 | 0.001 |
| 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".