Bibliographic record
Abstract
Alpha-1 antitrypsin (AAT) deficiency is an under-recognized hereditary disorder associated with the premature onset of chronic obstructive pulmonary disease, liver cirrhosis in children and adults, and less frequently, relapsing panniculitis, systemic vasculitis and other inflammatory, autoimmune and neoplastic diseases. Severe AAT deficiency mainly affects Caucasian individuals and has its highest prevalence (1 : 2000-1 : 5000 individuals) in Northern, Western and Central Europe. In the USA and Canada, the prevalence is 1: 5000-10 000. Prevalence is five times lower in Latin American countries and is rare or nonexistent in African and Asian individuals. The key to successful diagnosis is by measuring serum AAT, followed by the determination of the phenotype or genotype if low concentrations are found. Case detection allows implementation of genetic counselling and, in selected cases, the application of augmentation therapy. Over the past decade, it has been demonstrated that AAT is a broad-spectrum anti-inflammatory, immunomodulatory, anti-infective and tissue-repair molecule. These new capacities are promoting an increasing number of clinical studies, new pharmacological formulations, new patent applications and the search for alternative sources of AAT (including transgenic and recombinant AAT) to meet the expected demand for treating a large number of diseases, inside and outside the context of AAT deficiency.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".