A validated case definition for chronic rhinosinusitis in administrative data: a Canadian perspective
Bibliographic record
Abstract
BACKGROUND: Pharmacoepidemiological research using administrative databases has become increasingly popular for chronic rhinosinusitis (CRS); however, without a validated case definition the cohort evaluated may be inaccurate resulting in biased and incorrect outcomes. The objective of this study was to develop and validate a generalizable administrative database case definition for CRS using International Classification of Diseases, 9th edition (ICD-9)-coded claims. METHODS: A random sample of 100 patients with a guideline-based diagnosis of CRS and 100 control patients were selected and then linked to a Canadian physician claims database from March 31, 2010, to March 31, 2015. The proportion of CRS ICD-9-coded claims (473.x and 471.x) for each of these 200 patients were reviewed and the validity of 7 different ICD-9-based coding algorithms was evaluated. RESULTS: The CRS case definition of ≥2 claims with a CRS ICD-9 code (471.x or 473.x) within 2 years of the reference case provides a balanced validity with a sensitivity of 77% and specificity of 79%. Applying this CRS case definition to the claims database produced a CRS cohort of 51,000 patients with characteristics that were consistent with published demographics and rates of comorbid asthma, allergic rhinitis, and depression. CONCLUSION: This study has validated several coding algorithms; based on the results a case definition of ≥2 physician claims of CRS (ICD-9 of 471.x or 473.x) within 2 years provides an optimal level of validity. Future studies will need to validate this administrative case definition from different health system perspectives and using larger retrospective chart reviews from multiple providers.
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How this classification was reachedexpand
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 | 0.060 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".