Hemochromatosis: Platelets and aspartate aminotransferase are useful high-degree fibrosis marker #
Bibliographic record
Abstract
We read with great interest the article by Crawford et al.1 about the utility of hyaluronic acid with ferritin in the prediction of cirrhosis for C282Y hemochromatosis. It is a very important advance for noninvasive fibrosis prediction in hemochromatosis patients. We would like to add that in their study they included 56 hereditary hemochromatosis patients, and in a cohort of 48 for whom data on serum ferritin, platelet count, and aspartate aminotransferase (AST) values were available, they applied Beaton et al.'s predictive model2 and compared their findings with those from French and Canadian hereditary hemochromatosis populations. In this cohort, 26 patients had serum ferritin > 1000 μg/L, and in 15 of the patients, the platelet count was >200,000 × 103/mL. Two of the 15 patients had cirrhosis, and both had raised AST. It is stated that the combination of a platelet count < 200,000, ferritin > 1000, and raised AST failed to detect 30% (3/10) of the patients with cirrhosis (they did not have all the predicting factors), but it seems that none of the patients with cirrhosis had a platelet count > 200,000 and normal AST. We recently reported the utility of various noninvasive methods for fibrosis prediction in hemochromatosis, with 32 patients included and nine presenting stage F3 or F4 fibrosis (four patients had cirrhosis).3 In our study, the combination of raised AST and a platelet count < 200,000 revealed a negative predictive value of 100% for high-degree fibrosis. Platelets alone had a 94% negative predictive value for high-degree fibrosis; the four patients with cirrhosis had a platelet count < 200,000, and three of them had a serum ferritin value < 1000 (three were homozygous for C282Y, and one lacked the hemochromatosis gene study). We think that the combination of a platelet count < 200,000 and raised AST is a useful tool for cirrhosis prediction in hemochromatosis. Noninvasive markers for fibrosis prediction are a very interesting field of investigation in hemochromatosis, but perhaps results differ in different populations. Agustin Castiella*, Eva Zapata*, Pedro Otazua , * Hepatogastroenterology, Mendaro Hospital, Mendaro, Spain, Hepatogastroenterology, Mondragon Hospital, Mondragon, Spain.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".