Superiority of virtual microscopy versus light microscopy in transplantation pathology
Bibliographic record
Abstract
Virtual microscopy has begun to change conventional pathology practice. We tested the reliability of this new technology in transplantation pathology. We studied 40 kidney transplant biopsies for cause and compared reproducibility of Banff scores using virtual slides versus glass slides. Three glass slides per biopsy were scanned as high-resolution digital slides using Aperio ScanScope. Three pathologists independently reviewed the biopsies: twice by glass slides and twice by virtual slides. Eleven histopathological lesions were scored and used to construct diagnosis according to Banff criteria. The intra-observer reproducibility of Banff scores was substantially good using either virtual slides or glass slides (mean κ: 0.69 vs. 0.64, p>0.05). The inter-observer reproducibility of Banff scores was better in virtual slides than in glass slides (mean κ: 0.42 vs. 0.28, p<0.001). Among the lesions, transplant glomerulopathy scoring by virtual slides showed the highest inter-observer reproducibility, with a similar accuracy to glass slides. The agreement for acute rejection between virtual and glass slides was not different from the agreement between two readings of glass slides. Thus, virtual microscopy is a reliable and more reproducible technology and has several advantages over glass slides, e.g., accessibility via internet, no fading. We recommend virtual microscopy for transplant diagnostics, including utilization for clinical trials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".