Bibliographic record
Abstract
Autoimmune hepatitis (AIH) is an inflammatory disease of the liver, secondary to a loss of immune tolerance against liver antigens resulting in a progressive destruction of the hepatic parenchyma. 1 The article from Porta et al. in the present issue describes the clinical, laboratory, and histologic features of a large cohort of children with AIH, and includes an analysis of the treatment response and outcome. 2 This multicenter study presents the results of a retrospective revision of medical records from 828 children, representing the largest series in the world literature.A main characteristic of AIH is its fluctuant course, partially explaining the delay between the first symptoms or signs and the diagnosis of the disease, delaying the beginning of the immunosuppressive treatment, thus increasing the risk of developing cirrhosis and liver failure. 1 In the present series, the time recorded between onset and diagnosis was 11 and 15 months for AIH type 1 and type 2, respectively.To avoid such delay, AIH should be included in the differential diagnosis of any liver anomaly, from fortuitous discovery of high serum aminotransferases to signs of chronic liver disease, keeping in mind that spontaneous partial improvement
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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".