NEUROLOGIC IMPAIRMENT DUE TO VITAMIN E AND COPPER DEFICIENCIES IN CELIAC DISEASE
Bibliographic record
Abstract
Celiac disease is an immune-mediated intolerance to gluten, which is found in grains such as wheat and barley. Its estimated prevalence is ∼1% and it remains underdiagnosed in many countries. It causes an enteropathy leading to macro- and/or micronutrient malabsorption, most commonly of iron, vitamin B12, vitamin D, and calcium. Deficiencies of only one or a few nutrients are the most common manifestation of non-diarrheal, “silent” celiac disease, which accounts for 50% of initial presentations.1 Immune- and nonimmune mechanisms provoke diverse neurologic complications in celiac disease including peripheral neuropathy and ataxia.2 ### Case report. We investigated a 58-year-old French-Canadian man with 1 year of progressive leg stiffness and gait unsteadiness. He had mild, longstanding, biopsy-proven celiac disease presenting with dermatitis herpetiformis in 1980. He had received replacement therapy for iron-deficiency anemia and vitamin B12 deficiency during the last decade. There were no other symptoms of celiac disease and he did not adhere to a gluten-free diet. Two of five siblings have celiac disease without neurologic symptoms. CSF and urine examinations were normal. Blood studies revealed a mild microcytic anemia (131 g/L) with acanthocytes; mildly elevated transaminases; serum α-tocopherol (vitamin E) 2.22 × 10−3); serum ceruloplasmin 200 mg/L); …
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".