Predicting sulcus size using ocular measurements
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it