The Accuracy and Precision Performance of Four Videokeratoscopes in Measuring Test Surfaces
Bibliographic record
Abstract
In this study we evaluated the accuracy and precision of three placido-disk videokeratoscopes (the Keratron, Medmont and TMS) and one videokeratoscope that uses the raster-stereogrammetry technique (PAR-CTS) in elevation topography with six test surfaces. The test surfaces were a sphere, an asphere, a multicurve, and three bicurve surfaces. Each instrument performed well on certain test surfaces, but none of the instruments excelled on all of the surfaces. The results showed high accuracy of the Keratron and Medmont instruments in measuring the sphere, asphere, and multicurve surfaces, but not the bicurve surfaces. The precision of the Keratron and Medmont instruments were high. The TMS and PAR-CTS instruments showed poorer accuracy than the Keratron and Medmont instruments for the multicurve test surface but showed better performance for the bicurve surfaces. The PAR-CTS had the poorest performance in precision of the four instruments. The use of the Noryl spherical test surface instead of polymethyl methacrylate (PMMA) resulted in small differences in the accuracy performance of the placido-disk videokeratoscopes only.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".