Methods of defining hypertension in electronic medical records: validation against national survey data
Bibliographic record
Abstract
BACKGROUND: Electronic medical records (EMR) can be a cost-effective source for hypertension surveillance. However, diagnosis of hypertension in EMR is commonly under-coded and warrants the needs to review blood pressure and antihypertensive drugs for hypertension case identification. METHODS: We included all the patients actively registered in The Health Improvement Network (THIN) database, UK, on 31 December 2011. Three case definitions using diagnosis code, antihypertensive drug prescriptions and abnormal blood pressure, respectively, were used to identify hypertension patients. We compared the prevalence and treatment rate of hypertension in THIN with results from Health Survey for England (HSE) in 2011. RESULTS: Compared with prevalence reported by HSE (29.7%), the use of diagnosis code alone (14.0%) underestimated hypertension prevalence. The use of any of the definitions (38.4%) or combination of antihypertensive drug prescriptions and abnormal blood pressure (38.4%) had higher prevalence than HSE. The use of diagnosis code or two abnormal blood pressure records with a 2-year period (31.1%) had similar prevalence and treatment rate of hypertension with HSE. CONCLUSIONS: Different definitions should be used for different study purposes. The definition of 'diagnosis code or two abnormal blood pressure records with a 2-year period' could be used for hypertension surveillance in THIN.
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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 | 0.162 | 0.340 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".