Choroidal Thickness and Biometric Markers for the Screening of Lacquer Cracks in Patients with High Myopia
Bibliographic record
Abstract
OBJECTIVES: Validation of choroidal thickness and other biometrics measured by spectral domain optical coherence tomography (SD-OCT) in predicting lacquer cracks formation in highly myopic eyes. METHODS: Patients with a refractive error worse than -8 diopters and moderate myopic maculopathy were recruited into two groups based on the presence or absence of lacquer cracks (36 eyes without and 33 eyes with lacquer cracks). Choroidal thickness, refractive error, and axial length were measured and subjected to receiver operating characteristic curve analysis to identify the optimal cutoff values at predicting lacquer crack formation. The width of the retinal pigment epithelium (RPE), RPE to the inner segment/outer segment line, RPE to the external limiting membrane were also measured and compared to the subfoveal choroidal thickness to assess their relationships as potential markers of lacquer crack formation. RESULTS: Lacquer crack is associated with decreased choroidal thickness, lower best-corrected visual acuity, longer axial length and higher refractive errors. Choroidal thickness has the strongest association with lacquer crack formation versus axial length and refractive error. In eyes with lacquer cracks, stellate lacquer cracks are associated with thinner choroidal thickness compared to eyes with linear lacquer cracks. Subfoveal choroidal thickness less than the width of the retinal pigment epithelium to the inner segment/outer segment line is also associated with lacquer crack formation (sensitivity 78.8%, specificity 88.3%, and accuracy 81.2%). CONCLUSIONS: This study suggests that choroidal thickness and other SD-OCT measurements could be employed clinically to predict the development and severity of lacquer cracks in patients with high myopia.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 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".