Alpha-1 Antitrypsin Phenotyping: An Unmet Educational Need of Healthcare Providers
Bibliographic record
Abstract
Background: Diagnosing alpha-1 antitrypsin deficiency (A1ATD) involves two-step laboratory testing, determination of serum alpha-1 antitrypsin (A1AT) level and phenotyping if A1AT < 100 mg/dL. Whether these guidelines are effectuated in clinical practice is uncertain. To begin to address this issue, we determined whether A1AT phenotyping is performed in patients with serum A1AT 57 - 99 mg/dL at our institution. Methods: We reviewed the medical records of patients seen at Jesse Brown Veterans Affairs Medical Center from January 2019 to October 2022 with serum A1AT between 57 and 99 mg/dL. In each case, pertinent demographic, clinical, and pulmonary function tests data were extracted. Data were presented as means and standard deviation (SD) where appropriate. The Student's t -test was used for statistical analysis. P < 0.05 was considered statistically significant. Results: Thirty patients (90% males; 60 ± 18 years) with serum A1ATD < 100 mg/mL were identified. Fourteen were African Americans, four Hispanics, and 12 non-Hispanic Whites. The majority were current or ex-smokers. Fourteen (47%) patients had lung disease, 14 (47%) liver disease and one had concomitant lung and liver diseases. Mean ± SD forced expiratory volume in 1 s (FEV 1 ) and lung diffusing capacity were 2.57 ± 1.41 L (67±19% predicated) and 18.7 ± 10 mL/min/mm Hg (64±28% predicted), respectively. Only 13 patients (43%) underwent phenotype testing (seven African Americans, five Whites, and one Hispanic). Six patients had MZ phenotype, four MS, and three SZ. One patient died from acute respiratory failure during the study period. Conclusions: Phenotyping of patients with serum A1AT 57 - 99 mg/dL at our institution is inadequate. Accordingly, regular continuous medical educational programs on A1AT phenotyping targeting healthcare providers are warranted. J Clin Med Res. 2024;16(2-3):124-127 doi: https://doi.org/10.14740/jocmr5111
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".