Calretinin Staining in Anorectal Line Biopsies Accurately Distinguished Hirschsprung Disease in a Retrospective Study
Bibliographic record
Abstract
INTRODUCTION: The absence of submucosal ganglion cells does not reliably distinguish Hirschsprung disease from non Hirschsprung disease in anorectal line biopsies. Calretinin staining might be helpful in these biopsies. To determine its value, we analyzed calretinin positive mucosal neurites in anorectal line biopsies. METHODS: Two pediatric pathologists, without access to patient data, evaluated calretinin positive mucosal neurites in anorectal line junctional mucosa in archival rectal biopsies contributed by 17 institutions. A separate investigator compiled patient information and sent data for statistical analysis. RESULTS: Biopsies with anorectal junctional mucosa from 115 patients were evaluated for calretinin positive mucosal neurites. 20/20 Hirschsprung disease biopsies were negative. 87/88 non Hirschsprung disease biopsies and 7/7 post pullthrough Hirschsprung disease neorectal biopsies were positive. Statistical analysis of the 108 non pullthrough biopsies yielded an accuracy of 99.1% (sensitivity 100%, specificity 98.9%). Age range was preterm to 16 years. Biopsy size was less than 1 mm to over 1 cm. CONCLUSIONS: Absence of calretinin positive mucosal neurites at the anorectal line was highly accurate in distinguishing Hirschsprung disease from non Hirschsprung disease cases in this blinded retrospective study. Calretinin staining is useful for interpreting biopsies from the physiologic hypoganglionic zone up to the anorectal line.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".