MP093STANDARDIZED ASSESSMENT OF DIGITAL RENAL BIOPSY WHOLE SLIDE IMAGES
Bibliographic record
Abstract
Introduction and Aims: Advances in digital image acquisition, processing, and data storage are enabling whole slide imaging (WSI) to revolutionize histopathological assessment. Efficient international collaborations, development of morphologic scoring systems and case review are facilitated by online access and webinar-based meetings, without lengthy travel or mailing of unreplaceable material. In addition, digital softwares allow annotation of specific structures (glomeruli) to support the application of accurate quantitative scoring systems for correlation to clinical and genetic findings. The EURenOmics pathology study group, as part of the INTErnational digital nephRopAThology nEtwork (INTEGRATE), is devoted to implement standardization, accuracy and reproducibility of the renal biopsy morphologic profile, by participating in worldwide webinar sections. Methods: As part of the EURenomics project we collected 171 kidney biopsies from children with steroid-resistant nephrotic syndrome enrolled in 13 centers of the PodoNet registry. 652 deidentified glass slides were scanned into WSI at high resolution (40-fold magnification, Hamamatsu Nanozoomer). To implement accuracy of glomerular evaluation, glomeruli were identified and given a unique number (annotated), even if present on several sections. A scoring system developed by the NEPTUNE study group for adult nephrotic syndrome was modified, extended and specified in mutual discussions. It includes 60 glomerular descriptors, 7 tubulo-interstitial and vascular parameters as well as 16 ultrastructural and 10 immunofluorescence items. To test reproducibility the scoring was performed independently by 6 pathologists.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".