Enhanced resolution of interstitial fibrosis in pediatric renal allograft biopsies using image analysis of trichrome stain
Bibliographic record
Abstract
Birk PE, Gill JS, Blydt‐Hansen TD, Gibson IW. Enhanced resolution of interstitial fibrosis in pediatric renal allograft biopsies using image analysis of trichrome stain. Pediatr Transplantation 2010: 14: 925–930. © 2010 John Wiley & Sons A/S. Abstract: The Banff classification is ill suited to detect subtle histologic progression in renal allografts. We present image analysis methodology to precisely quantify IF in pediatric renal allograft biopsies routinely stained with MT. The mean area %IF was determined in 105 pediatric renal allograft biopsies. Associations between %IF or Banff ci scores and estimated GFR were determined using GEE modeling. Logistic regression was used to estimate IF progression. Percent IF (mean ± s.d.) was 6.83% ± 3.94, 10.39 ± 5.23%, and 20.53 ± 8.74 in patients with ci0, ci1, and ci2, respectively. The difference in %IF between biopsies with ci0, ci1, and ci2 was not proportionately incremental: compared to ci2, ci0 had 67% less IF (p < 0.0001), while ci1 had 48% less IF (p < 0.0001). AR had no impact on the precision of %IF measurements. Each 0.5% decrement in %IF was associated with a 1 mL/min per 1.73 m2 increase in GFR (p < 0.004). Histologic progression was demonstrated by increasing %IF values (p < 0.0001) and could be estimated by IF = 2.61 × (months) + 6.43. This readily adaptable methodology may be used for the longitudinal assessment of IF in pediatric protocol renal allograft 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".