Training and Assessment of Clinician’s Utilization of the Los Angeles Classification for Reflux Esophagitis
Bibliographic record
Abstract
Background: Endoscopic recognition of reflux esophagitis is critical for the evaluation and treatment of patients with gastroesophageal reflux disease; however, there are limited data on the need for education to minimize interobserver disagreement in clinical practice. Methods: We created an educational program for the LA classification on the International Working Group for the Classification of Oesophagitis (IWGCO) website that included endoscopic video recordings of the distal esophagus. Participants completed an entry survey before the training module and were able to proceed to subsequent training videos once they provided the correct LA classification. Participants then completed a test module—an 80% score was required to pass. Descriptive analyses and regression analyses were performed to analyze data. Results: In the entry survey, 83/90 (92%) participants reported using the LA classification for the majority or all patients with reflux symptoms. However, only 31/90 (34%) participants reported feeling very or completely confident in the use of the LA classification. Only 3/71 (4.2%) participants correctly classified all 9 training videos on their first attempt. The testing module was completed by 60 participants, 16 (26.7%) of whom passed after one attempt, with 32 (53.3%), 8 (13.3%), and 1 (1.7%) passing after 2, 3, and 4 attempts, respectively. There was no significant correlation between the number of attempts to successfully pass the testing module and participant characteristics. Conclusion: Even after a training module, >75% of participants required more than one attempt to correctly classify the test videos. Further structured education around the LA classification is needed.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".