Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it