Validity of Administrative Data for the Diagnosis of Primary Sclerosing Cholangitis: A Population-Based Study
Bibliographic record
Abstract
Purpose: Few studies have investigated the epidemiology of Primary Sclerosing Cholangitis (PSC) using administrative databases because information regarding the validity of the diagnostic codes is not available. Additionally, the codes for PSC (576.1 in ICD-9-CM and K83.0 in ICD-10) are not distinct, including common conditions such as ascending cholangitis. The objectives of this study were to assess the validity of the diagnostic codes for PSC in administrative data and generate a coding algorithm to identify PSC cases. Methods: Two study populations were identified: (1) from administrative databases; and (2) through review of medical records. The administrative data study population consisted of PSC cases residing in the Calgary Health Region (CHR; population ˜1.2 million) from 2000-2003. All patient contacts/claims with a diagnostic field coded 576.1 or K83.0 were extracted from three health service databases: (1) physician claims; (2) hospital discharge; and (3) emergency visits and ambulatory procedures (e.g., ERCP). Because residents of CHR have universal health care we were able to link these databases through unique personal health numbers. The chart review study population was defined as all residents of the CHR with a diagnosis of PSC between 2000 and 2003. Using the chart review as the reference standard, the sensitivity (Se) and positive predictive value (PPV) and their 95% CIs of a PSC diagnosis based on administrative data was calculated. Coding algorithms were developed by considering variables associated with PSC (e.g., coexistent IBD, procedures [e.g., ERCP], etc.), and PSC coding details (i.e., frequency of a PSC code) in order to maximize the PPV while maintaining a high Se. Results: A total of 86 confirmed PSC cases were identified from chart review and 998 potential PSC cases were identified from the three health service databases. In the administrative databases, 14 true cases were not captured resulting in Se and PPV estimates of 84% (95% CI 74%, 91%) and 7% (6%, 9%), respectively. When considering only inpatient data, 49 true cases were not captured resulting in Se and PPV estimates of 43% (32%, 54%) and 9% (6%, 12%), respectively. The optimal coding algorithm included one PSC code and one IBD code when all three databases were combined with corresponding Se and PPV estimates of 56% (45%, 66%) and 59% (48%, 70%), respectively. The Se and PPV estimates obtained when applying this algorithm to only inpatient data were 28% (19%, 39%) and 71% (53%, 85%), respectively. Conclusion: An algorithm for the accurate identification of true PSC cases from administrative data could not be derived. Thus, a distinct diagnostic code for PSC is required to facilitate investigation of the epidemiology of PSC using administrative data.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".