Iron‐induced mucosal injury to the upper gastrointestinal tract
Bibliographic record
Abstract
AIMS: To define the causes and associations of mucosal iron deposition in upper gastrointestinal biopsy specimens and to describe the morphological features of iron-related injury. METHODS: The histological pattern, intensity and distribution of iron in biopsies obtained from 1991 to 2005 were recorded and correlated with endoscopic and clinical findings. RESULTS: Twenty-five biopsies (16 gastric, four duodenal, five oesophageal) were accrued. Iron deposition was seen in two groups: 10 cases showed erosive injury, with brown-black crystalline material overlying eroded epithelium. These patients were taking oral iron tablets. The remaining 15 cases showed variable iron deposition in the surface epithelium, lamina propria and glands. In nine patients, there was a history of oral iron intake and at least eight had had blood transfusions. The most intense iron deposition was noted in patients with end-stage liver disease. The mean age of patients with erosive injury was 43% higher than in the iron overload group (76 versus 53 years). Iron stains were also performed on 15 normal gastric biopsies and five biopsies with chronic, non-specific gastritis; all were negative for haemosiderin deposition. CONCLUSIONS: Iron-related erosive injury is related to oral iron pill ingestion and occurs in older patients. Mucosal iron deposition is also associated with iron overload disorders.
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 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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".