An Automated Blood Pressure Display for Self-Measurement in Patients With Chronic Kidney Disease (iHealth Track): Device Validation Study
Bibliographic record
Abstract
BACKGROUND: Hypertension is a global public health issue and is closely related to chronic kidney disorder (CKD). In people with CKD, strict monitoring of blood pressure is an important part of therapy. OBJECTIVE: The aim of this research was to validate the iHealth Track blood pressure monitoring device for patients with CKD according to the European Society of Hypertension International Protocol 2010 (ESH-IP2). METHODS: In total, 33 patients who received hemodialysis in Plasencia participated in the study. There were 9 successive measurements made, which conformed to the ESH-IP2. We calculated the differences between the standard reference device (Omron M3 Intellisense) and the test device (iHealth Track) for blood pressure and heart rate values. For 99 total comparisons of paired measurements, we classified differences into various categories (≤5 mmHg, ≤10 mmHg, and ≤15 mmHg for blood pressure; ≤3, ≤5, and ≤8 beats per minute for heart rate). RESULTS: In 90 of 99 systolic blood pressure and 89 of 99 diastolic blood pressure comparisons between the devices, measurement differences were within 5 mmHg. In 81 of 99 heart rate comparisons between the devices, measurement differences were within 3 beats per minute. The mean differences between the test and reference standard measurements were 3.27 (SD 2.99) mmHg for systolic blood pressure, 3.59 (SD 4.55) mmHg for diastolic blood pressure, and 2.18 (SD 2.75) beats per minute for heart rate. We also observed that for both systolic and diastolic blood pressure, 31 of 33 participants had at least two of three comparisons between the devices with measurement differences less than 5 mmHg. For heart rate, 28 of 33 patients had at least two of three comparisons between the devices with measurement differences less than 3 beats per minute. CONCLUSIONS: To our knowledge, this is the first study to show that iHealth Track meets the requirements of the ESH-IP2 in patients with CKD. Therefore, the iHealth Track is suitable for use in renal patients.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".