Assessing family history of chronic disease in primary care: Prevalence, documentation, and appropriate screening.
Bibliographic record
Abstract
OBJECTIVE: To assess the proportion of primary care patients who report a family history (FH) of type 2 diabetes, coronary artery disease, breast cancer, or colorectal cancer (CRC); assess concordance of FH information derived from the electronic medical record (EMR) compared with patient-completed health questionnaires; and assess whether appropriate screening was informed by risk based solely on FH. DESIGN: Data from the BETTER (Building on Existing Tools to Improve Chronic Disease Prevention and Screening in Primary Care) trial were used. Patients were mailed questionnaires. Baseline FH and screening data were obtained for enrolled patients from the EMR and health questionnaires. SETTING: Ontario and Alberta. PARTICIPANTS: Randomly selected patients from 8 family practices. MAIN OUTCOME MEASURES: Agreement on FH between the EMR and questionnaire was determined; logistic regression was used to assess significant predictors of screening. RESULTS: In total, 775 of 789 (98%) patients completed the health questionnaire. The mean age of participants was 52.5 years and 72% were female. A minimum of 12% of patients (range 12% to 36%) had a reported FH of 1 of 4 chronic diseases. Among patients with positive FH, the following proportions of patients had that FH recorded in the EMR compared with the questionnaire: diabetes, 24% in the EMR versus 36% on the questionnaire, κ = 0.466; coronary artery disease, 35% in the EMR versus 22% on the questionnaire, κ = 0.225; breast cancer, 21% in the EMR versus 22% on the questionnaire, κ = 0.241; and CRC, 12% in the EMR versus 14% on the questionnaire, κ = 0.510. There was moderate agreement for diabetes and CRC. The presence of FH was a significant predictor of CRC screening (odds ratio 1.9, 95% CI 1.1 to 3.1). CONCLUSION: A moderate prevalence of FH was found for 4 conditions for which screening recommendations vary with risk based on FH. Having patients self-complete an FH was thought to be feasible; however, questions about FH accuracy and completeness from both self-report and EMR remain. Work is needed to determine how to facilitate the adoption of FH tools into practice as well as strategies linking familial risk to appropriate screening.Trial registration number ISRCTN07170460 (ISRCTN Registry).
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".