Validation of the Toronto Upper Gastrointestinal Cleaning Score in children
Bibliographic record
Abstract
OBJECTIVES: Gastroscopy is used to examine the upper gastrointestinal (GI) tract, but no validated method yet exists to assess the quality of mucosal visualization in children. Utilizing validated endoscopic scales can enhance study quality and standardization across centers. This study aimed to validate the existing Toronto Upper Gastrointestinal Cleaning Score (TUGCS) in pediatric patients. METHODS: This was a multicenter, prospective, single-masked study conducted in 10 European pediatric gastroenterology centers. Endoscopists with varying degrees of experience assessed the quality of mucosal visualization in prerecorded gastroscopies using the TUGCS. Each endoscopist assessed the studies two times in random order, with an interval of at least 2 weeks. The correlations of individual and total scores were statistically compared between themselves, between assessors, and between assessment attempts. Internal consistency was also checked with Cronbach's α. RESULTS: Seventeen endoscopists participated in the study. The TUGCS demonstrated high consistency within raters, with a score of 0.64 (95% confidence interval [CI]: 0.34-0.84), and an excellent test-retest reliability of 0.97 (95% CI: 0.94-0.99). The scale also showed high internal consistency, with a Cronbach's α of 0.95. The correlation between different items ranged from 0.60 to 0.77, and the correlation between individual items and the total score ranged from 0.66 to 0.88. No significant differences in the assessment were found based on the raters' experience performing endoscopy, specialization, age, or gender. The endoscopists found TUGCS easy to learn and potentially useful, especially in clinical trials. CONCLUSIONS: The TUGCS was demonstrated as a reliable and validated method for assessing the visualization quality of the upper GI mucosa in pediatric patients.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".