Relationship Between Gastroesophageal Reflux Disease and Endoscopic Finding “Iodine-Unstained Streak”
Bibliographic record
Abstract
BACKGROUND: Esophagogastroduodenoscopy (EGD) with iodine stain is a useful and diffused method for diagnosing esophageal cancer. We can perform the procedure easily with endoscopic system which does not comprise image-enhanced endoscopy. Several studies advocated that iodine-unstained streaks are a characteristic finding of gastroesophageal reflux disease (GERD). However, there are only a few reports about the subject. In this study, we investigated the usefulness of iodine chromoendoscopy for GERD consultation. METHODS: The study was conducted with 154 GERD cases in which EGD with iodine stain to the esophagus was performed. For the 154 cases, we analyzed the existence of reflux esophagitis finding and iodine-unstained streaks. In 47 GERD cases (proton pump inhibitor (PPI): 45 cases, histamine H2-receptor antagonist (H2-RA): two cases) where medication was started after EGD, we examined predictive factors of the symptom improvement such as sex, age, weight, reflux esophagitis finding, and iodine-unstained streak. RESULTS: An iodine-unstained streak was observed in 50/154 cases (32.5%). For 50 cases with iodine-unstained streak, there were only 24/50 cases (48.0%) that had both reflux esophagitis findings (≥ Los Angeles classification: grade M) and an iodine-unstained streak. For 47 cases in which medication was started, 34 cases showed improvement in their symptoms, and 13 cases did not show improvement. An iodine-unstained streak was observed more often in "Improved" group rather than in "Not improved" group (P < 0.01). When we supposed an iodine-unstained streak to be the predictive factor of the medication effect for GERD, sensitivity was 61.8% and specificity was 84.6%. CONCLUSIONS: No erosion was often found in the GERD cases without reflux esophagitis, and iodine-unstained streak was observed more often in "Improved" group rather than in "Not improved" group. We think that iodine-unstained streak can be useful for diagnosing of GERD and predictive factor of the medication effect.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".