Prediction of esophageal and gastric histology by macroscopic diagnosis during upper endoscopy in pediatric celiac disease
Bibliographic record
Abstract
AIM: To determine the sensitivity of macroscopic appearance for predicting histological diagnosis at sites other than duodenum in pediatric celiac disease (CD). METHODS: Endoscopic and histologic findings in pediatric patients undergoing upper endoscopy for first-time diagnosis of CD at Stollery Children's Hospital from 2010-2012 were retrospectively reviewed. RESULTS: Clinical charts from 140 patients were reviewed. Esophageal and gastric biopsies were taken in 54.3% and 77.9% of patients, respectively. Endoscopic appearance was normal in the esophagus and stomach in 75% and 86.2%. Endoscopic esophageal diagnoses were eosinophilic esophagitis (EE) (11.8%), esophagitis (7.9%), glycogenic acanthosis (1.3%) and non-specific abnormalities (3.9%). Endoscopic gastric diagnoses were gastritis (8.3%), pancreatic rest (0.9%), and non-specific abnormalities (4.6%). Histology was normal in 76.3% of esophageal and 87.2% of gastric specimens. Abnormal esophageal histology was EE (10.5%), esophagitis (10.5%), glycogenic acanthosis (1.3%) and non-specific (1.3%). Gastritis was reported in 12.8% of specimens. Sensitivity and specificity of normal endoscopy for predicting normal esophageal histology was 86.2% and 61.1%, and for normal gastric histology was 87.4% and 21.4%. CONCLUSION: In the absence of macroscopic abnormalities, routine esophageal and gastric biopsy during endoscopy for pediatric CD does not identify major pathologies. These findings have cost and time saving implications for clinical practice.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".