Morphometry of Cells and Guttae in Subjects With Normal or Guttate Endothelium With a Contour Detection Algorithm
Bibliographic record
Abstract
PURPOSE: To develop a semiautomatic method to analyze morphology of cells and guttae in corneal endothelium. METHODS: Specular endothelial pictures from 42 and 21 subjects with healthy and guttate corneas, respectively, were analyzed independently by two observers with cell contour-extracting routines. One observer also analyzed healthy endothelia with the Corner method (Bambi). Differences between observers and between methods in mean cell area (MCA), coefficient of variation (CV), and percentage of cells with five, six, or seven sides were tested for significance with paired t tests. The Contour analysis of pictures with guttae included their mean area. RESULTS: There were no significant differences in MCA, CV, or the percentage of cells with five, six, or seven sides between the measurements obtained on repeated analysis by the same observer or on a second analysis performed by a different observer with the Contour method. However, the differences between the Contour and Bambi methods were statistically significant for MCA (337.5 +/- 37.7 vs. 327.7 +/- 36.5), CV (0.32 +/- 0.05 vs. 0.30 +/- 0.05), and percentage of cells with six and seven sides, but not for the percentage of five-sided cells. In subjects with guttata, the MCA was 561 +/- 170 microm, and the mean area of guttae was 1,538 +/- 849 microm. CONCLUSIONS: This detection algorithm is repeatable and reproducible, and it generates a cell border overlay useful in analyzing the morphology of cells and guttae. The analysis of corneal guttae could become a useful follow-up procedure to discriminate between patients with corneal guttata and Fuchs dystrophy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".