Predicting sulcus size using ocular measurements
Bibliographic record
Abstract
PURPOSE: To predict sulcus size using ocular measurements. SETTING: Michel Pop Clinics, Montreal, Quebec, Canada. METHODS: Forty-three eyes were evaluated using several techniques. Ultrasound biomicroscopy (UBM) echograms were taken to measure the anterior chamber depth (ACD), sulcus size, and central corneal thickness. The limbus size was measured with a caliper. Axial length, ACD, and pachymetry were measured by contact ultrasonography. Refraction and corneal power were also evaluated. RESULTS: The coefficient of linear regression was 0.05 between the limbus and the sulcus size (P =.78), 0.76 between ultrasonography and UBM ACD measurements (P <.001), and 0.69 between ultrasonography and UBM pachymetry (P <.001). Paired t tests showed that ultrasound and UBM ACD measurements were not statistically different (P =.70) but that ultrasound and UBM pachymetry measurements were (P <.001). The sulcus versus limbus difference was 0.6 mm for myopia and 0.3 mm for hyperopia. A backward elimination multiple regression performed with all measures to predict sulcus size resulted in the following formula: Sulcus size = 18.9 - 0.023 x sphere + 0.15 x mean keratometry (R = 0.49; P =.005; statistical power = 0.89; standard error of estimate = 0.5 mm). CONCLUSION: Traditional estimation of sulcus size through limbal measurement is inadequate because limbus size alone cannot predict sulcus size. A general formula using the sphere and the mean corneal power can help predict sulcus size. Corneal power was significantly and negatively correlated with sulcus and limbus size as well as sphere. The standard error of sulcus measurement by UBM was 0.4 mm.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".