An integrated system to compensate for temperature drift and ageing in non-invasive blood pressure measurement
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
Accurate blood pressure (BP) measurement is critical in the diagnosis and management of hypertension, as an error of 5 mmHg can be responsible for doubling or halving the number of patients diagnosed with this condition. Sensor drift, due to changing environmental factors, such as ambient temperature, can contribute to the inaccuracy. Studies also show that long term sensor drift, or ageing, can lead to a change of almost 9 mmHg in blood pressure measurement during the first three months of usage. In this work, a new stage is added to current cuff-based BP devices. This stage is responsible for adjusting the pressure reading before displaying it to end users, by monitoring changes in the ambient temperature and sensor ageing and adaptively compensating for these inaccuracies. These sources of inaccuracy are suppressed using algorithms based on Empirical Mode Decomposition (EMD), which has the feature of removing unwanted noise components without affecting the phase or the frequency distribution of the measured signal.
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) | 0.000 | 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