Noninvasive continuous blood pressure monitoring using microelectromechanical system technology
Bibliographic record
Abstract
BACKGROUND: Continuous blood pressure monitoring is essential in the management of patients in critical conditions, as well as those under anesthesia. However, continuous blood pressure monitoring requires insertion of a catheter into the radial artery. Thus, continuous noninvasive arterial blood pressure monitoring would be ideal. PARTICIPANTS AND METHODS: We designed and built a continuous noninvasive arterial blood pressure monitoring device with a pressure sensor diaphragm using microelectromechanical system technology, a square with 4 mm sides that were 0.4 mm thick. Comparisons between a continuous noninvasive arterial blood pressure monitoring device and a sphygmomanometer were carried out on 92 volunteers, and comparisons between noninvasive and invasive blood pressure monitoring were performed on three patients perioperatively at Fukushima Medical University Hospital. RESULTS: In the comparisons of arterial blood pressure measurements between a sphygmomanometer and our device, the differences became gradually greater over time after starting continuous monitoring in conscious participants. In the comparisons of arterial blood pressure measurements between the invasive and noninvasive methods in unconscious subjects under general anesthesia, the results of noninvasive monitoring were consistent with those of invasive arterial blood pressure monitoring. CONCLUSION: Continuous noninvasive arterial monitoring with a pressure sensor diaphragm using microelectromechanical system technology is a possible alternative to conventional invasive arterial pressure monitoring by an arterial catheter.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".