Monocular Clinical Outcomes and Range of Near Vision following Cataract Surgery with Implantation of an Extended Depth of Focus Intraocular Lens
Bibliographic record
Abstract
IMPORTANCE: A full range of near and intermediate vision has not been clinically evaluated for the Symfony extended depth of focus intraocular lens (EDOF IOL). BACKGROUND: To evaluate the monocular range of near visual acuity with an EDOF IOL. DESIGN: Retrospective case series. PARTICIPANTS: Consecutive patients of a single surgeon from January 2017 through March 2018. METHODS: Phacoemulsification with implantation of an EDOF IOL. MAIN OUTCOME MEASURES: Uncorrected distance visual acuity (UDVA), uncorrected near visual acuity (UNVA), corrected distance visual acuity (CDVA), distance-corrected near visual acuity (DCNVA), range of DCNVA, and optimal near focal length. RESULTS: Seventy-six eyes of 48 patients (34 or 71% female, mean age: 68 years) were included with a mean follow-up of 68 days. Mean values were as follows: logarithm of the minimum angle of resolution (logMAR) UDVA 0.02 ± 0.09, logMAR UNVA 0.12 ± 0.09 at a mean distance of 51 cm, logMAR CDVA -0.05 ± 0.07, logMAR DCNVA 0.08 ± 0.07 at a mean distance of 51 cm, and a spherical equivalent of -0.16 diopters ±0.35. Percentage of eyes achieving DCNVA of 20/30 were 84% at 36 cm, 92% at 41 cm, 99% at 51 cm, 93% at 61 cm, and 74% at 71 cm. DCNVA of 20/40 or better was achieved in nearly 100% of eyes over a range of 35 cm. CONCLUSIONS AND RELEVANCE: The Symfony EDOF IOL achieved excellent distance visual acuity while providing a 35 cm range of near visual acuity at levels useful for many tasks in nearly all patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".