AstigMATIC: an automatic tool for standard astigmatism vector analysis
Bibliographic record
Abstract
BACKGROUND: Standardization for reporting medical outcomes facilitates clinical study comparisons and has a fundamental role on research reproducibility. In this context, we present AstigMATIC, a free standalone application for automated standardized astigmatism vector analyses in corneal and intraocular refractive surgeries. AstigMATIC uses a simple graphical user interface (GUI) and allows the simultaneous display and analysis of astigmatism magnitude and axis. RESULTS: The software produces the four following standard graphs according to the standards of the Alpins Method; 1-Target-Induced Astigmatism Vector, 2- Surgically-Induced Astigmatism Vector, 3-Difference Vector and 4-Correction Index. Vector means with X and Y standard deviations are automatically calculated and displayed on the corresponding single-angle vector plots (0 to 180°). Data points are entered into a simplified GUI with no need for command line input. The standard graphs can be easily exported as high-resolution TIFF images for figures to use in production and presentations. CONCLUSIONS: AstigMATIC enables the user to easily and efficiently analyze vectorial astigmatism outcomes using the standardized Alpins Method for post-surgical astigmatism.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.011 |
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".