Noninvasive prediction of cirrhosis in C282Y-linked hemochromatosis
Bibliographic record
Abstract
The aim of the present study was to examine the predictive accuracy of noninvasive clinical and biochemical variables associated with cirrhosis among patients with C282Y homozygous hemochromatosis. Sixteen clinical and laboratory variables were recorded at the time of diagnosis in 193 Canadian C282Y homozygous patients. All patients underwent percutaneous liver biopsy and 27 (14%) had biopsy specimen-proven cirrhosis. Prediction of cirrhosis was assessed first by univariate regression analysis. Variables significantly related to cirrhosis were then evaluated by stepwise linear multivariate regression. Receiver operating characteristic curve analysis of the most informative variables from multivariate analysis was then used to devise a clinically applicable index for the noninvasive prediction of cirrhosis. This index was then validated in 162 C282Y homozygous patients in France. Ferritin, blood platelets, and aspartate transaminase (AST) level were selected for the clinical index. The combination of ferritin levels of 1,000 microg/L or greater, platelet levels of 200 x 10(9)/L or less, and AST levels above the upper limit of normal led to a correct diagnosis of cirrhosis in 77% of Canadian patients. In the French patients, this led to a correct diagnosis of cirrhosis in 90%. In conclusion, in C282Y homozygous patients, a combination of easily measured laboratory variables (ferritin, platelets, AST) can be used to make the diagnosis of cirrhosis in approximately 81% of cases, reducing the need for liver biopsy.
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 | 0.004 |
| 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) | 0.001 | 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".