Underdiagnosis of Alpha‐1 Antitrypsin Deficiency in Cirrhotic Liver Transplant Candidates: Findings From a Multicenter Retrospective Study
Bibliographic record
Abstract
BACKGROUND AND AIMS: Alpha-1 antitrypsin deficiency (AATD) is a prevalent genetic disorder in Europe causing hepatic fibrosis and often remains undiagnosed, even in severe cases requiring liver transplantation (LT). This study aimed to determine the frequency of pre-LT diagnosis amongst LT candidates with AATD and to describe their clinical characteristics. A secondary goal was to assess awareness and practices concerning AATD amongst LT specialists in France. METHODS: This retrospective multicenter cohort study included LT candidates diagnosed with AATD based on PAS-positive staining of explanted livers (1995-2020) from nine centres in France and Canada. A 22-question survey was sent to LT specialists in France to assess AATD knowledge and practices. RESULTS: Amongst 58 patients diagnosed with AATD between 1996 and 2020, 40% were diagnosed pre-LT, 15% post-LT and 45% never confirmed. Less than 25% had non-specific pulmonary symptoms. The survey revealed poor awareness of AATD; 78% of specialists rated their knowledge as very low to moderate. Consistent pre-LT screening occurred in 59.3% of cases, and 52.5% recommended familial screening upon a confirmed diagnosis. CONCLUSION: AATD remains underdiagnosed in pre-LT assessments and is poorly understood amongst practitioners in France. Improved screening can enhance patient management, especially with emerging potentially curative treatments.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".