Comparison of ocular biometry and refractive outcome between a new and classic optical biometer
Bibliographic record
Abstract
PURPOSE: To compare the ocular biometry and refractive outcome between 2 optical biometers. SETTING: Zhongshan Ophthalmic Center, Guangzhou, China. DESIGN: Prospective observational study. METHODS: 953 patients with cataract underwent preoperative biometry including ZW-30 sum-of-segments (SOS) method (ZW SM ), ZW-30 composite method (ZW CM ), and IOLMaster 700. Agreement of axial length (AL), with or without Cooke-modified AL (CMAL) adjustment, was analyzed using Bland-Altman 95% limits of agreement (LoA). Subgroup analysis was used based on ALs (short eyes: AL <22 mm; normal eyes: 22 mm ≤ AL <26 mm; long eyes: AL ≥26 mm). Refractive prediction accuracy was evaluated using the Emmetropia verifying optical (EVO) 2.0 formula and its SOS-optimized version (EVO 2.0 SOS ). RESULTS: In short and normal eyes, narrow 95% LoAs (<0.2 mm) were identified among 3 ALs. However, AL obtained by ZW SM was lower compared with this obtained by ZW CM and IOLMaster 700 (95% LoA -0.39 to 0.01 mm; -0.38 to 0.04 mm) in long eyes. CMAL adjustment enhanced the agreement of AL between ZW SM and ZW CM (95% LoA -0.01 mm to 0.02 mm), ZW SM and IOLMaster 700 (95% LoA -0.10 mm to 0.07 mm) in long eyes. Myopic prediction errors (PE) have been identified in the use of ocular biometric parameters obtained from ZW SM (mean PE [ME]: EVO 2.0, -0.19 diopters [D]; EVO 2.0 SOS , -0.18 D). After adjusting ME to zero, no difference was observed in PE calculated using any combination of formulas based on biometric measurements from 3 devices. CONCLUSIONS: This new segmented biometer demonstrated excellent agreement with IOLMaster 700 in short and normal eyes. However, ALs obtained by IOLMaster 700 are not interchangeable with the SOS method and require CMAL adjustment in long eyes. The application of the SOS method's ocular biometric parameters in refractive prediction led to myopic errors, which suggests constant optimization.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".