Bibliographic record
Abstract
BACKGROUND: Previous studies have examined the relevance of hypertension (HTN) screening in walk-in clinics. So far, no valid algorithm has been proposed on how to integrate HTN screening in this context. The aim of our study was to assess, in a walk-in clinic setting, the HTN screening strategy for performing an automated office blood pressure (AOBP) measurement following an initially high office blood pressure (OBP) measurement. PATIENTS AND METHODS: Included participants were adults with nonemergent medical conditions and an initial walk-in clinic OBP between systolic 140 and/or diastolic 90 mmHg and systolic 180 and/or diastolic 110 mmHg. AOBP was performed with patients unattended. The 24-h ambulatory blood pressure measurement (ABPM) was used as the diagnostic threshold. RESULTS: Fifty participants were included in the study. The overall HTN prevalence as confirmed by the 24-h ABPM was 46% [95% confidence interval (CI): 32.19-59.81]. After an elevated OBP, AOBP over diagnostic thresholds occurred in 32 patients and were confirmed by ABPM in 20 participants, leading to a 62.5% positive predictive value (95% CI: 51.5-72.3%). Measurements under the AOBP diagnostic threshold occurred in 18 patients and were confirmed by ABPM in 15 participants, leading to a negative predictive value of 83.3% (95% CI: 62.3-93.8%). CONCLUSION: In a walk-in clinic, an elevated OBP is a useful screening tool due its ability to recognize nearly one in two patients as actually hypertensive. Adding an AOBP makes it possible to specify what course of action to take. This ultimately results in better targeting of patients for an ABPM referral.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".