Untreated Hypertension and the Emergency Department: A Chance to Intervene?
Bibliographic record
Abstract
OBJECTIVES: Untreated hypertension (HTN) is a major public health problem. Screening for untreated HTN in the emergency department (ED) may lead to appropriate treatment of more patients. The authors investigated the accuracy of identifying HTN in the ED, the proportion of ED patients with untreated HTN, patient characteristics predicting untreated HTN, and provider documentation of untreated HTN. METHODS: The authors performed a retrospective cross-sectional study on a random sample of 2,061 adults treated at an urban academic ED. The validity of six candidate definitions of HTN in the ED was assessed in a subsample using outpatient clinic records as the reference standard. "Untreated HTN" was HTN without a HTN medication listed in the ED history. "Documentation of untreated HTN was documentation of HTN as a visit problem, specific referral for HTN, or ED discharge with a HTN" information sheet or a HTN medication. Multivariable logistic regression was used to determine associations. RESULTS: The preferred definition of HTN in the ED had sensitivity of 86% (95% confidence interval [CI] = 80% to 90%), specificity of 78% (95% CI = 69% to 85%), and accuracy of 83% (95% CI = 78% to 87%). Of the 42% (95% CI = 40% to 44%) of ED patients with HTN, 43% (95% CI = 39% to 46%) had untreated HTN. Patients who were younger and male, without primary care physicians, with fewer prior ED visits, and without cardiovascular comorbidities, had higher odds of untreated HTN. Of those with untreated HTN, 8% (95% CI = 5% to 11%) had their untreated HTN documented. CONCLUSIONS: Untreated HTN was common in the ED but rarely documented. Providers can use ED blood pressures along with patient characteristics to identify those with untreated HTN for referral to primary care.
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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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".