Validating a case definition for adult asthma in primary care electronic medical records
Bibliographic record
Abstract
Although asthma is one of the most common chronic conditions affecting Canadians, its epidemiologic characteristics and burden in primary care contexts are poorly understood. The aim of this study was to develop and validate a case definition to identify adults with asthma who consult family physicians and to estimate the prevalence of asthma in that setting in Canada. This validation study utilized a database of electronic medical records (EMRs) from the Southern Alberta Primary Care Research Network, a node of the Canadian Primary Care Sentinel Surveillance Network (SAPCReN-CPCSSN). The population included patients over age 17y of any gender and health status who had visited an SAPCReN-CPCSSN primary care provider during the period December 1, 2014-December 31, 2016. The validation of the case definition involved comparing a case-finding algorithm to caseness determined by an expert physician review of the records of 1000 patient in the CPCSSN database. The case definition, which included the ICD-9 code 493 and asthma-related text words, had 83.33% sensitivity (95% CI: 63.61-93.88%), 99.28% specificity (95% CI: 98.51-99.67%), a positive predictive value of 74.07% (95% CI: 55.03-87.14%), and a negative predictive value of 99.59% (95% CI: 98.93-99.86%). The prevalence of adult asthma in CPCSSN primary care practices in southern Alberta was 4.20% (95% CI: 4.09-4.31). The strong validation metrics suggest that this case definition is valid for both clinical and research purposes. The validated case definition may be used to improve patient care and improve understanding of the prevalence and burden of asthma in primary care in Canada.
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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 | 0.079 | 0.211 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".