Validity of administrative data for the diagnosis of primary sclerosing cholangitis: a population-based study
Bibliographic record
Abstract
BACKGROUND/AIMS: Administrative databases could be useful in studying the epidemiology of primary sclerosing cholangitis (PSC); however, there is no information regarding the validity of the diagnostic code in administrative databases. The aims of this study were to determine the validity of administrative data for a diagnosis of PSC and generate algorithms for the identification of PSC patients. METHODS: The sensitivity (Se) and positive predictive value (PPV) of a PSC diagnosis based on administrative data from 2000 to 2003 were determined through chart review data. Algorithms were developed by considering variables associated with PSC and coding details. A logistic regression model was constructed using covariates associated with PSC. Based on this model, each subject was assigned a probability of having PSC. A cutoff value was selected that maximized the Se and specificity (Sp) of correctly predicting PSC cases. RESULTS: In the administrative data, the initial Se and PPV were 83.7 and 7.2% respectively. The optimal algorithm included one PSC code and one inflammatory bowel disease code and had Se 56% and PPV 59%. Overall, the algorithms yielded inadequate PPV and Se estimates to identify a cohort of true PSC cases. The predictive model was constructed using six covariates. For this model, the area under the receiver operating characteristic curve was 93.5%. A cutoff of 0.0729 was used, which maximized the Se 81.9% and Sp 90.7%; however, the PPV was 41.0%. CONCLUSION: An algorithm for the identification of true PSC cases from administrative data was not possible. We recommend that PSC receives a distinct ICD code from ascending cholangitis.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".