A systematic review of validated methods for identifying pancreatitis using administrative data
Bibliographic record
Abstract
PURPOSE: To systematically review algorithms identifying cases of pancreatitis in administrative data, with a focus on studies examining algorithm validity. METHODS: A literature search was conducted using PubMed and the Iowa Drug Information Service database. Reviews were conducted by two investigators identifying studies using data sources from the USA or Canada. These data sources most likely reflect the coding practices of Mini-Sentinel data partners. RESULTS: Eight studies were obtained examining the validity of an algorithm to identify pancreatitis in either hospital or ambulatory medical records or billing databases. The best-performing algorithm was International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) code 577.0; with a positive predictive value of 60%-80% and a negative predictive value usually greater than 90%. Populations involved in different studies were heterogeneous; age ranges, level of population risk, pancreatitis etiology, and geographic distribution were highly variable. CONCLUSIONS: Validation studies find that the principal ICD-9-CM diagnosis code of 577.0 had the best positive predictive value and specificity. Current studies do not support the use of the ICD-9-CM codes 577.1 and 577.2. Databases enhanced with laboratory values at point of care would invariably increase the specificity of existing algorithms.
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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 | 0.013 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.001 |
| 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".