The use of digital microscopy method for comparison of thicknesses between normal corneas and ex vivo rejected corneal grafts: focus on Descemet membrane
Bibliographic record
Abstract
Objective: To analyze and compare thickness measurements of corneal layers, especially the Descemet membrane (DM), in normal corneas and in failed grafts due to rejection (FGRs) using a digital microscopy method. Methods: An experimental, cross-sectional, and analytical study was performed at the Henry C. Witelson Ocular Pathology Laboratory (McGill University Health Center and Research Institute, Montreal/Canada). Slides of 25 normal human corneas and 40 FGRs were examined using a Philips Ultra Fast Scanner® and the associated software. The inclusion criteria adopted were samples diagnosed as normal corneas or FGRs, all specimens were from patients older than 18 years of age. Slides with corneal structures that could not be adequately visualized and/or whose donor epidemiological information could not be obtained were excluded from the study. On each slide, the thickness of the corneal layers was measured, with 2 central measurements, 2 measurements at the nasal periphery, and 2 measurements at the temporal periphery using perpendicular planes as reference. Results: There were differences between the normal and FGR groups in the means of the central thickness of the epithelium (p<0.001), the nasal and temporal stroma regions (p<0.001), and the DM in the nasal and temporal regions (p<0.001). Comparing the mean thicknesses of the different regions (central, nasal and temporal) of the DM in the same group, the central region of the DM in the normal corneas had a lower mean thickness than the two peripheral regions (p<0.001), a difference that did not occur in the FGR group. Conclusions: Normal corneas had a lower epithelium thickness in the central region than did corneas in the FGR group. In addition, the stroma and DM thickness of the nasal and temporal periphery was significantly higher in normal corneas than in those from the FGR group.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".