Performance of the international classifications criteria for autoimmune hepatitis diagnosis in Mexican patients
Bibliographic record
Abstract
The revised score of the International Autoimmune Hepatitis Group (R-IAIHG) and the simplified criteria (SC) are used for diagnosis of autoimmune hepatitis (AIH). Our aim is to evaluate the performance of these classifications to differentiate AIH from other autoimmune liver diseases. The frequency of diagnosis of definite AIH was similar both by the R-IAIHG and the SC systems (41% versus 40%), whereas diagnosis of probable AIH was made more commonly by the R-IAIHG than the SC (59% versus 29%), and 23 patients that have been graded as definite (n = 7) or probable (n = 16) AIH by the R-IAIHG had non-diagnostic scores by the SC system. The scoring systems rendered concordant diagnosis of definite (n = 15) and probable (n = 13) AIH in 28/73 patients (38%). Discordant diagnoses of AIH were rendered in 45/73 patients (62%). The R-IAIHG exhibited a sensitivity of 95%, specificity of 90%, and positive predictive value (PPV) and negative predictive value (NPV) of 93% for both. On the other hand, the SC had a lower sensitivity (65%) but a higher specificity (100%), PPV of 100%, and NPV of 68%. In conclusion, both international scoring systems diagnosed the same number of cases as definite AIH. The R-IAIHG showed a higher sensitivity in diagnosing AIH, whereas the SC showed a higher specificity. SC are easier to apply at the bedside and exclude more patients that could have a different etiology.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".