Validity of diagnostic codes and liver‐related laboratory abnormalities to identify hepatic decompensation events in the Veterans Aging Cohort Study
Bibliographic record
Abstract
PURPOSE: The absence of validated methods to identify hepatic decompensation in cohort studies has prevented a full understanding of the natural history of chronic liver diseases and impact of medications on this outcome. We determined the ability of diagnostic codes and liver-related laboratory abnormalities to identify hepatic decompensation events within the Veterans Aging Cohort Study (VACS). METHODS: Medical records of patients with hepatic decompensation codes and/or laboratory abnormalities of liver dysfunction (total bilirubin ≥ 5.0 g/dL, albumin ≤ 2.0 g/dL, INR ≥ 1.7) recorded 1 year before through 6 months after VACS entry were reviewed to identify decompensation events (i.e., ascites, spontaneous bacterial peritonitis, variceal hemorrhage, hepatic encephalopathy, hepatocellular carcinoma) at VACS enrollment. Positive predictive values (PPVs) of diagnostic codes, laboratory abnormalities, and their combinations for confirmed outcomes were determined. RESULTS: Among 137 patients with a hepatic decompensation code and 197 with a laboratory abnormality, the diagnosis was confirmed in 57 (PPV, 42%; 95%CI, 33%-50%) and 56 (PPV, 28%; 95%CI, 22%-35%) patients, respectively. The combination of any code plus laboratory abnormality increased PPV (64%; 95%CI, 47%-79%). One inpatient or ≥2 outpatient diagnostic codes for ascites, spontaneous bacterial peritonitis, or variceal hemorrhage had high PPV (91%; 95%CI, 77%-98%) for confirmed hepatic decompensation events. CONCLUSION: An algorithm of 1 inpatient or ≥ 2 outpatient codes for ascites, peritonitis, or variceal hemorrhage has sufficiently high PPV for hepatic decompensation to enable its use for epidemiologic research in VACS. This algorithm may be applicable to other cohorts.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".