Validity of diagnostic codes and liver‐related laboratory abnormalities to identify hepatic decompensation events in the Veterans Aging Cohort Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it