Administrative coding for alpha-1 antitrypsin deficiency including the pi*ZZ phenotype is accurate in Sweden
Bibliographic record
Abstract
Background We aimed to validate the International Classification of Diseases (ICD)-10 codes for alpha-1 antitrypsin deficiency (AATD) (E880A = asymptomatic AATD; E880B = symptomatic AATD) in a large hospital reporting to the Swedish National Patient Register and to ascertain their relation to the protease inhibitor (Pi)*ZZ-phenotype.Methods We randomly selected 150 adults and 50 children who visited Karolinska University Hospital (Stockholm, Sweden) between 2014 and 2024 with coding for E880A or E880B (1:1). Positive predictive values (PPVs) of AATD ICD-10 codes were calculated for correctly assigned codes and the Pi*ZZ-phenotype using medical charts as gold standard. Information on smoking status, lung disease, liver disease, and living area, were also retrieved.Results The PPV of AATD ICD-10 codes (E880A + E880B) in adults was 99% (95%CI = 95–100%; n = 148/150). The PPV for the Pi*ZZ-phenotype was only 59% (95%CI = 50–67; n = 83/141) but increased to 79% (95%CI = 67–88%; n = 50/63) when only considering outpatients with E880B coding. Of adult participants, 13% had liver disease, 51% had lung disease, and 50% were ever-smokers. In children, the PPV of E880A + E880B was 100% (95%CI = 91–100%; n = 50/50) for any AATD diagnosis and was 88% for the Pi*ZZ-phenotype (95%CI = 75–95%; n = 43/49). Liver or lung disease occurred in 6% of children. Results were consistent across several sensitivity analyses.Conclusion In a tertiary care setting, the validity of ICD-10 codes for AATD is excellent. The PPV of these codes for delineating the Pi*ZZ-phenotype is high in children but requires an algorithm in adults with coding for E880B in outpatients.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".