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Record W4415790211 · doi:10.1186/s12876-025-04363-3

Autoimmune gastritis: clinical and histological study in a Peruvian population

2025· article· en· W4415790211 on OpenAlexaff
Fernando Arévalo, Soledad Rayme, Romy Rolando, Rocío Ramírez, Jaime Fustamante, Eduardo Monge, Pedro Montes, J Sebastian Miranda Maravi

Bibliographic record

VenueBMC Gastroenterology · 2025
Typearticle
Languageen
FieldMedicine
TopicHelicobacter pylori-related gastroenterology studies
Canadian institutionsCytodiagnostics (Canada)
Fundersnot available
KeywordsHepatologySerologyAutoimmune GastritisPopulationGastritisAutoimmune diseaseEpidemiology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.036
GPT teacher head0.337
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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