Evaluation of the B/F Ratio Distribution of Cataract Patients Without Corneal Abnormalities and Its Effect on Ocular Parameters
Bibliographic record
Abstract
PURPOSE: To evaluate the distribution of the posterior-anterior corneal radius ratio (B/F ratio; posterior corneal radius/anterior corneal radius) in patients without corneal abnormalities, and to investigate which parameters affect this ratio. METHODS: Five thousand eyes from 5,000 patients who underwent cataract surgery were recruited to this study. We explored the linear relationship between B/F ratio and 13 variables using Principal Component-Multivariate Linear Regression Analysis. RESULTS: The B/F ratio was negatively correlated with the difference between simulated keratometry (SimK) and true net power (TNP), central corneal thickness, spherical aberration (SA), and posterior corneal astigmatism and positively correlated with posterior corneal radius, corneal posterior surface, axial length (AL) and anterior corneal radius. Several variables (central corneal thickness, difference between SimK and TNP and asphericity coefficient (Q-value) of the posterior corneal surface) had the highest loading on the final score. B/F ratio reflects the refractive state and anatomical structure of the cornea: thus, higher B/F ratios were associated with larger posterior corneal surface curvature radius, longer axial length, thinner central corneal thickness, lower high order aberrations of the cornea and SA, and the numerical difference between simK and TNP gradually reduced. In clinical practice, for patients with lower B/F ratio, special care should be taken in the choice of system used for intraocular lens (IOL) measurements.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".