Primary care provider diagnosed eczema within electronic medical records from seven canadian provinces
Bibliographic record
Abstract
Context: Most epidemiological research on eczema has largely relied on patient survey data. With the increasing use of electronic medical records (EMR) in primary care, there has been a shift in epidemiological research towards the use of validated case definitions to study disease. Objective: Apply a validated case definition for eczema to EMR data from primary care providers participating in the Canadian Primary Care Sentential Surveillance Network (CPCSSN) to determine the prevalence of diagnosed eczema in Canada and describe patient's characteristics including risk factors and comorbidities. Study Design: Cross-sectional study. Dataset: EMR data from 1,574 primary care providers in seven Canadian provinces. Population Studied: Patient records were examined for those with at least one encounter with a family physician, nurse practitioner or community pediatrician participating in CPCSSN between January 1, 2017, and December 31, 2019 (N= 689,301 patients). Outcome Measures: Primary outcome was lifetime prevalence of eczema. Secondary outcomes were demographics of eczema patients and the association between eczema and various comorbidities. Results: Descriptive statistics revealed a lifetime prevalence of documented eczema of 11.6% overall, 15.1% in those <19 years, and 11.5% in those >19 years. Patients with eczema were more likely to be smokers. Using the Material and Social Deprivation Index we found eczema was more prevalent among the least materially and socially deprived quintiles. In logistic regression, female patients (OR, 1.29; 95% CI, 1.27-1.32) and patients <19 years (OR, 1.27; 95% CI, 1.19-1.35) had higher odds of eczema compared to male patients and patients aged >19 years. Patients with comorbidities such as rhinitis (OR, 2.11; 95% CI, 2.06-2.17), asthma (OR, 1.4; 95% CI, 1.37-1.43), any allergy (OR, 1.09, 95% CI 1.06-1.11), COPD (OR, 1.1; 95% CI, 1.06-1.14) and anxiety (OR, 1.66; 95% CI, 1.63-1.69) had higher odds of eczema compared to patients without these comorbidities. Depression (OR, 0.96; 95% CI, 0.94-0.98) and obesity (OR, 0.96; 95% CI, 0.94-0.98) were negatively associated with a diagnosis of eczema. Conclusion: This is the first study in Canada to determine the prevalence of primary care provider documented eczema using EMR data. This study can inform and improve disease surveillance as well as future studies exploring burden of illness, trends or interventions related to eczema 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".