The Toronto Upper Gastrointestinal Cleaning Score: a prospective validation study
Bibliographic record
Abstract
Background Assessment of mucosal visualization during esophagogastroduodenoscopy (EGD) can be improved with a standardized scoring system. To address this need, we created the Toronto Upper Gastrointestinal Cleaning Score (TUGCS). Methods We developed the TUGCS using Delphi methodology, whereby an international group of endoscopy experts iteratively rated their agreement with proposed TUGCS items and anchors on a 5-point Likert scale. After each Delphi round, we analyzed responses and refined the TUGCS using an 80 % agreement threshold for consensus. We used the intraclass correlation coefficient (ICC) to assess inter-rater and test–retest reliability. We assessed internal consistency with Cronbach’s alpha and item-total and inter-item correlations with Pearson’s correlation coefficient. We compared TUGCS ratings with an independent endoscopist’s global rating of mucosal visualization using Spearman’s ρ. Results We achieved consensus with 14 invited participants after three Delphi rounds. Inter-rater reliability was high at 0.79 (95 %CI 0.64–0.88). Test–retest reliability was excellent at 0.83 (95 %CI 0.77–0.87). Cronbach’s α was 0.81, item-total correlation range was 0.52–0.70, and inter-item correlation range was 0.38–0.74. There was a positive correlation between TUGCS ratings and a global rating of visualization (r = 0.41, P = 0.002). TUGCS ratings for EGDs with global ratings of excellent were significantly higher than those for EGDs with global ratings of fair (P = 0.01). Conclusion The TUGCS had strong evidence of validity in the clinical setting. The international group of assessors, broad variety of EGD indications, and minimal assessor training improves the potential for dissemination.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".