Concordance Between Clinical and Pathological Diagnosis of Stromal Corneal Dystrophies in a Large Case Series
Bibliographic record
Abstract
PURPOSE: To assess the frequency and histopathological features of stromal corneal dystrophies. In addition, we sought to evaluate the concordance between the clinical diagnoses provided by ophthalmologists and the pathological reports. METHODS: We retrospectively analyzed all cases of stromal corneal dystrophies between 1996 and 2022. Data were collected from pathological reports of corneal buttons obtained from penetrating keratoplasties (PK). Clinical and pathological diagnoses along with demographic information were recorded. Concordance between clinical and pathological diagnoses was assessed using frequency analysis and Cohen kappa coefficient. RESULTS: Histopathological review of 1440 corneal specimens from PK revealed 56 (3.8%) stromal corneal dystrophies in 53 patients. The most common dystrophy found was lattice corneal dystrophy (LCD), present in 30 specimens (53.6%), followed by granular corneal dystrophy type 2 in 14 specimens (25.0%), granular corneal dystrophy type 1 in 7 specimens (12.5%), and macular corneal dystrophy in 5 specimens (8.9%). Concordance between clinical and pathological diagnoses was observed in 37 of 45 cases, resulting in an overall concordance rate of 82.2% with a Cohen kappa coefficient of 0.64. CONCLUSIONS: The concordance rate of 82.2% and Cohen kappa coefficient of 0.64 indicate strong agreement between clinical and pathological diagnoses of stromal corneal dystrophies. However, despite ranking as the second most common dystrophy, the absence of clinical diagnoses for granular corneal dystrophy type 2 highlights the critical need to use both Masson Trichrome and Congo red stains when granular or lattice-like deposits are present in the cornea to ensure precise and reliable diagnosis.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".