mEYEstro software: an automatic tool for standardized refractive surgery outcomes reporting
Bibliographic record
Abstract
BACKGROUND: Standardization for reporting medical outcomes enables clinical study comparisons and has a fundamental role in research reproducibility. In this context, we present mEYEstro, a free novel standalone application for automated standardized refractive surgery graphs. mEYEstro can be used for single and multiple group comparisons in corneal and intraocular refractive surgery patients. In less than 30 s and with minimal user manipulation, mEYEstro automatically creates the required journal standard graphs while simultaneously performing valid statistical analyses. RESULTS: The software produces the following 11 standard graphs; Efficacy: 1. Cumulative uncorrected (UDVA) and corrected visual acuity (CDVA), 2. Difference between UDVA and CDVA, Safety: 3. Change in line of CDVA, Accuracy: 4. Spherical equivalent (SEQ) to intended target, 5. Attempted vs. achieved SEQ, 6. Defocus equivalent (DEQ) accuracy, 7. Refractive astigmatism accuracy, 8. Target-induced astigmatism vs. Surgically-induced astigmatism, 9. Correction index histogram, 10. Angle of error histogram, Stability: 11. SEQ stability over time. Percent proportions, means, standard deviations, Cohen's d effect sizes, and p-values are calculated and displayed on each graph. All graphs can be easily exported as high-resolution TIFF images for figures to use in scientific manuscripts and presentations. CONCLUSIONS: mEYEstro software enables clinicians, surgeons, and researchers, to easily and efficiently analyze refractive surgery outcomes using the standardized methodology required by several peer-reviewed ophthalmology journals.
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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 | 0.025 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.076 | 0.020 |
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".