How a cloud based platform can make ambulatory blood pressure monitoring more efficient, accessible, and evidence based
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
Ambulatory blood pressure measurement (ABPM) is the gold-standard method for blood pressure assessment. However, it is markedly underutilized, in part because legacy software provided with ABPM devices is archaic and inefficient. Herein, we illustrate an example of a recently developed cloud-based ABPM platform. Such a platform offers several distinct advantages: (1) the ability to guide users through the testing process; (2) synchronizing inputs of the technician, patient, physician, and administrative assistant so that testing can be successful and efficient; (3) providing guideline-concordant study interpretations that can be e-signed, reducing physician interpretation times; (4) enabling central expert oversight and peripheral deployment of testing, thereby increasing accessibility of quality testing; and (5) facilitating integration into electronic medical records, improving dissemination of results. It is envisioned that increased use of cloud-based ABPM platforms will lead to the expansion of quality ABPM testing, thus improving the care of patients with known or suspected hypertension.
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 | 0.001 | 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 | 0.001 | 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) | 0.000 | 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