Hirschsprung’s disease: Historical notes and pathological diagnosis on the occasion of the 100<sup>th</sup>anniversary of Dr. Harald Hirschsprung’s death
Bibliographic record
Abstract
Hirschsprung's disease (HSCR) or congenital megacolon is one of the differential diagnoses of chronic constipation mostly in infancy and may indeed represent a challenge for pediatricians, pediatric surgeons, and pediatric pathologists. The diagnosis relies clearly on the identification of the absence of ganglion cells at the plexuses (submucosus and myentericus) of the bowel wall. HSCR is usually located at the terminal (distal) rectum with potential pre-terminal or proximal extension to the less distal large bowel (sigmoid colon). Astonishingly, there is some evidence that Hindu surgeons of prehistoric India may have been exposed and had considerable knowledge about HSCR, but this disease is notoriously and eponymously named to Dr. Harald Hirschsprung (1830-1916), who brilliantly presented two infants with fatal constipation at the Berlin conference of the German Society of Pediatrics more than one century ago. Historical milestones and diagnosis of HSCR (originally called "Die Hirschsprungsche Krankheit") are reviewed. More than 100 years following his meticulous and broad description, HSCR is still a puzzling disease for both diagnosis and treatment. HSCR remains a critical area of clinical pediatrics and pediatric surgery and an intense area of investigation for both molecular and developmental biologists.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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".