Abstract P354: Performance of Different Oscillometric Blood Pressure Algorithms in Patients with Chronic Kidney Disease
Bibliographic record
Abstract
Objective: The extent to which different oscillometric blood pressure (BP) algorithms differ in how they derive BP in patients with chronic kidney disease (CKD) is unknown. We compared the performance of three different oscillometric algorithms against a known oscillometric reference standard in this patient population. Methods: Thirty intermittent hemodialysis (HD) patients and 30 stage 3 CKD (CKD) patients were recruited from a quaternary care, academic hospital in Edmonton, Canada. In random order, three sequential readings with an Omron HEM 907XL device and three sequential readings with a laptop-driven oscillometric device capable of detecting and recording oscillometric waveforms were obtained 30 seconds apart. The mean of each three reading set was used for analyses. Oscillometric algorithms (two fixed-ratio and one slope- based) were applied to the raw oscillometric data to derive BP. Paired t-tests were used to assess for statistical significance at the 0.05 level. Results: Mean age was 63.4 ± 16.3 y (HD group) and 65.9 ± 10.7 y (CKD group); percent female was 37% (HD) and 47% (CKD); mean BMI was 28.3 kg/m 2 (HD) and 30.3 kg/m 2 (CKD). Over 80% of participants in each group had hypertension. BP comparisons are summarized in Table 1. Conclusions: Varying the type of oscillometric algorithm results in markedly different systolic BP estimates in patients with CKD. The fixed-ratio algorithm produced results most comparable to the Omron device. These findings help clarify why different devices using different algorithms produce different results in patients with CKD.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".