Laboratory Procedures Update on Hirschsprung Disease
Bibliographic record
Abstract
OBJECTIVES: The detection of ganglion cells in rectal biopsies of infants or toddlers with severe constipation is routinely performed by pediatric pathologists in many institutions. Hirschsprung disease (HD) is defined by the lack of ganglion cells (aganglionosis). The early recognition and the prompt implementation of surgical procedures obviously protect infants affected with HD from potential life-threatening conditions, including enterocolitis and debilitating constipation. Image-based and non-image-based clinical techniques and some laboratory tests have been reevaluated along the years, but often fragmentarily. Immunohistochemical markers have been increasingly used in pathology laboratories to detect ganglion cells and nerve fibers. Recently, calretinin, a vitamin D-dependent calcium-binding protein with expression in ganglion cells and nerves, has been described as an adjunctive or primary diagnostic test in HD. The aim of the present study was to systematically summarize and update laboratory procedures targeting ganglion cells in rectal biopsies. METHODS: Procedures and tests have been reviewed and values of specificity and sensitivity have been calculated according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RESULTS: Contrast enema has the lowest sensitivity and specificity of all of the 3-index investigations under the lens: contrast enema, anorectal manometry, and biopsy with histology. The latter procedure seems to have the highest sensitivity and specificity. Acetylcholinesterase staining on fresh-frozen material has been found to have slightly higher rates of sensitivity and specificity when compared with hematoxylin and eosin only. Calretinin staining may be supportive for the diagnosis, although some cases with false-positivity may be of some concern. CONCLUSIONS: Hematoxylin and eosin with or without acetylcholinesterase remains the criterion standard according to our PRISMA-based data. In our opinion, the number of false-positive results with potential overtreatment may limit the increasing advocacy for calretinin staining. Both the "primum non nocere" dictum and the "loss aversion heuristic" need to be satisfied harmoniously by preventing harm from unnecessary surgery.
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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 | 0.013 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.017 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".