Endoscopic and clinical characteristics of autoimmune atrophic gastritis: Retrospective study
Bibliographic record
Abstract
Background and study aims: Autoimmune atrophic gastritis (AIG) is a rare chronic autoimmune disease characterized by gastric mucosa inflammation and atrophy. Limited clinical data exist about AIG, especially in western populations. In addition, there are no western series on the magnifying endoscopic features in AIG. This study presents a cohort of 63 patients with AIG, reporting their clinical, laboratory, and endoscopic findings. Patients and methods: A retrospective analysis was conducted on patients diagnosed with AIG at Kingston Health Sciences Centre, Canada, between January 2016 and December 2023. Data collected from medical records included age, sex, presenting symptoms, laboratory findings, endoscopic features, histopathology reports, and concomitant autoimmune diseases. Results: The study included 63 patients with autoimmune gastritis. Positive anti-parietal cell antibodies were found in the majority of patients (84.13%), whereas positive anti-intrinsic factor antibodies were less prevalent (25.40%). Deficiencies in vitamin B12 (49.21%) and iron (76.19%) were observed, along with a high prevalence of anemia (71.43%) and concomitant autoimmune diseases (58.73%). The dominant magnification pattern of atrophy in the body was oval/slit in 57.14% of patients (n=36), followed by tubular in 30.16% (n=19) and foveolar in 12.70% (n=8). Prevalence of neoplasia in our study was 42.86% (n=27). Conclusion: This study offers insights into the clinical, laboratory, and magnifying endoscopic features of patients with AIG. It demonstrates the three main magnifying endoscopic appearances of AIG and highlights the significant prevalence of gastric neoplasia, even in the low-risk Western population. These findings emphasize the importance of the endoscopic exam in identifying AIG and notably present the key magnifying endoscopy findings in a Western setting for the first time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".