Comparative Analysis of Axial Length Measurements by Optical Biometers Based on Partial Coherence Interferometry Versus Optical Low-Coherence Interferometry: An Office Audit
Bibliographic record
Abstract
Purpose In this study, we aimed to compare axial length (AL) measurements of the IOLMaster 500 (Carl Zeiss Meditec AG, Jena, Germany), based on partial coherence interferometry (PCI) versus the Aladdin (Topcon Healthcare, Oakland, NJ), based on optical low-coherence interferometry (OLCI), in a clinical setting. Methods A retrospective analysis of the records of patients presenting for cataract surgery at an ophthalmology practice between October 2019 and March 2020 was performed. All patients had biometry measurements on the IOLMaster 500 and the Aladdin. Data collected included patient demographics, cataract morphology and type, and AL measurements. The IOLMaster 500 and Aladdin measurements were compared using the unpaired t-test and Chi-squared test. Results In total, 393 eyes (197 patients) were included (91 males, 107 females) in the study. The IOLMaster 500 was unable to successfully obtain AL measurements in seven eyes (1.8%) and the Aladdin in 26 eyes (6.6%). The difference was statistically significant (p=0.0007). Advanced and central posterior subcapsular cataracts were common in eyes that had unsuccessful measurements. In the eyes successfully measured, the mean AL for the IOLMaster was 24.04 ±1.32 mm, while it was 24.04 ±1.34 mm for the Aladdin. However, this difference was not statistically significant (p=0.9165). Conclusion The IOLMaster 500 performed better in terms of the number of eyes for which AL measurements were successfully obtained compared to the Aladdin. This may be partly related to high volumes of advanced cataracts treated at our practice. However, this being a retrospective study, a cause-and-effect relationship could not be established.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".