EASL International Consensus Conference on Haemochromatosis
Bibliographic record
Abstract
The precise frequency of the different iron overload states due to haemochromatosis (HC) is unknown and need to be addressed in population studies ( 1. Bacon BR Powell LW Adams PC Kresina TF Hoofnagle JH Molecular medicine and haemochromatosis: at the crossroads. Gastroenterology. 1999; 116: 193-207 Abstract Full Text Full Text PDF PubMed Scopus (268) Google Scholar , 2. Pietrangelo A Haemochromatosis 1998: is one gene enough?. J Hepatol. 1998; 29: 502-509 Abstract Full Text PDF PubMed Scopus (30) Google Scholar ). The appropriate terminology for different iron overload states along the continuum is given below. The expert panel considers it both appropriate and feasible to define an over-accumulation state distinct from haemochromatosis. “Excess body iron storage” (iron overload) [haemosiderosis=iron staining in tissues] may be: i) Minimal: ∼1.5g (hepatic iron concentration, HIC, >30 μM/g) (?pathological significance: e.g. porphyria cutanea tarda (PCT); ii) Modest: 2–5 g (HIC>100 μM/g; serum ferritin approx 500 μg/l) (seen in chronic liver disease, haemolytic disorders, PCT, etc.); iii) Severe: >5 g (HIC >200 μM/g; serum ferritin approx 750 μg/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 | 0.032 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.026 | 0.017 |
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".