Autoimmune gastritis: clinical and histological study in a Peruvian population
Bibliographic record
Abstract
The diagnosis of autoimmune gastritis poses significant challenges, particularly in resource-limited settings where access to serological tests is restricted. This study aimed to evaluate the histological, endoscopic, and serological features of patients diagnosed with autoimmune gastritis in our population. METHODS: We retrospectively reviewed cases diagnosed with autoimmune gastritis at two medical centers in Peru. Clinical data, serological and endoscopic reports were collected for each case, and gastric mucosal tissue samples from the antrum and corpus were histologically examined. Immunohistochemistry was also performed to evaluate neuroendocrine hyperplasia. RESULTS: Histologically, all 44 cases exhibited atrophy in the corpus, with the majority presenting at advanced stages of the disease (84%). However, endoscopic findings did not correlate with histology, as only 59.09% of cases showed corpus atrophy on endoscopy. Immunohistochemical analysis revealed neuroendocrine hyperplasia in all cases (100%). Anti-intrinsic factor antibody was positive in only 25% of cases, whereas 84.1% showed positivity for anti-parietal cell antibodies. CONCLUSION: Histological evaluation of autoimmune gastritis cases demonstrates significant diagnostic potential, offering an effective alternative to costly and less accessible serological tests, particularly in resource-limited settings like ours.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".