Digital reporting of whole-slide images is safe and suitable for assessing organ quality in preimplantation renal biopsies
Bibliographic record
Abstract
Digital pathology allows networks of "remote" specialist pathologists to report the findings of preimplantation kidney biopsies. We sought to validate the assessment of preimplantation kidney transplant biopsies for diagnostic purposes using whole-slide images according to the recommendations of the College of American Pathologists. Sixty-two consecutive, previously reported, preimplantation kidney biopsies were scanned using the ScanScope Digital Slide Scanner at 0.5 μm/pixel (20× objective). The slides were assessed for percent glomerulosclerosis, tubular atrophy, interstitial fibrosis and vascular narrowing using the Remuzzi criteria by two pathologists, one using glass slides and the other using the whole-slide images viewed on a widescreen computer monitor. After a 2-week washout period, all of the slides were re-assessed by the same pathologists using the opposite mode of reporting to that used in the first evaluation. Very high glass-digital intraobserver concordance was achieved for the overall score and for individual grades by both pathologists (κ range, 0.841-0.973). The overall scores obtained by both pathologists and using both methods were identical. The times needed to assess the biopsies were 14 minutes when using a light microscope and 18 minutes, including scanning time, which averaged 2 minutes 20 seconds per slide, when using digital microscopy. Digital microscopy is a reliable, fast, and safe method for the assessment of preimplantation kidney biopsies.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".