Performance of an automated blood pressure measurement device in a stroke rehabilitation unit
Bibliographic record
Abstract
CONTEXT: Valid blood pressure (BP) measurements are needed in post-stroke rehabilitation hospital units for the management of hypertension. Automated devices could be used to improve on usual care BP measurement. However, more information is needed about the performance of these devices in such a context. METHODS: This prospective nonrandomized study was performed in stroke patients with hypertension hospitalized in a stroke rehabilitation unit. Two in-hospital BP assessment strategies were compared: usual care BP and in-hospital automated office BP (AOBP) standardized measurements. In-office AOBP and ambulatory BP monitoring (ABPM) were also performed on these patients. The main outcome was SBP. Study follow-up was until discharge, up to a maximum of 4 weeks. RESULTS: Sixty-two patients with stroke hospitalized in a rehabilitation unit were included. Usual care BP was 130 ± 12/79 ± 9 mmHg and differed from an in-hospital AOBP of 117 ± 14/75 ± 12 mmHg (P < 0.001/P < 0.001). In-hospital and in-office AOBP measurements did not differ. Twenty percent of patients reached SBP therapeutic goals according to in-hospital AOBP but not according to usual care BP measurements. CONCLUSION: This study shows that in a post-stroke rehabilitation unit, standardized in-hospital AOBP estimates are on average much lower than the usual care BP correlates and similar to the in-office AOBP estimates. In-hospital AOBP devices in a stroke rehabilitation unit could add important information for the management of hypertension.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".