How to check whether a blood pressure monitor has been properly validated for accuracy
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
Hypertension guidelines recommend that blood pressure (BP) should be measured using a monitor that has passed validation testing for accuracy. BP monitors that have not undergone rigorous validation testing can still be cleared by regulatory authorities for marketing and sale. This is the situation for most BP monitors worldwide. Thus, consumers (patients, health professionals, procurement officers, and general public) may unwittingly purchase BP monitors that are non-validated and more likely to be inaccurate. Without prior knowledge of these issues, it is extremely difficult for consumers to distinguish validated from non-validated BP monitors. For the above reasons, the aim of this paper is to provide consumers guidance on how to check whether a BP monitor has been properly validated for accuracy. The process involves making an online search of listings of BP monitors that have been assessed for validation status. Only those monitors that have been properly validated are recommended for BP measurement. There are numerous different online listings of BP monitors, several are country-specific and two are general (international) listings. Because monitors can be marketed using alternative model names in different countries, if a monitor is not found on one listing, it may be worthwhile cross-checking with a different listing. This information is widely relevant to anyone seeking to purchase a home, clinic, or ambulatory BP monitor, including individual consumers for use personally or policy makers and those procuring monitors for use in healthcare systems, and retailers looking to stock only validated BP monitors.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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