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PS-BPC03-6: USING A SIMPLE FIXED-RATIO BASED ALGORITHM MINIMIZES VARIABILITY IN OSCILLOMETRIC BLOOD PRESSURE DERIVATION IN PATIENTS WITH CHRONIC KIDNEY DISEASE

2023· article· en· W4315780466 on OpenAlexaff
Jennifer Ringrose, Kevan Smith, Afrooz Jalali, Harsimran Khinda, Isaac Wirzba, Hannah Ehce Ameh, Aminu K. Bello, Branko Braam, Raj Padwal

Bibliographic record

VenueJournal of Hypertension · 2023
Typearticle
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsChildren’s Health Research InstituteWomen and Children’s Health Research InstituteUniversity of Alberta
Fundersnot available
KeywordsMedicineKidney diseaseBlood pressureStage (stratigraphy)Linear regressionInternal medicineCardiologyStatisticsMathematics

Abstract

fetched live from OpenAlex

Objective: Oscillometric device accuracy may be decreased, and variability may be increased, in patients with chronic kidney disease. The objective of this study was to compare three oscillometric algorithms to identify which minimizes oscillometric blood pressure variability in chronic kidney disease. Design & Methods: Sixty patients with chronic kidney disease were studied. Thirty were on hemodialysis (HD group) and thirty had stage 3 chronic kidney disease (Stage 3 CKD). Baseline demographics and medical comorbidities were recorded. The mean of 3 readings from the Omron HEM 907XL device was used to provide a control (reference) blood pressure. Three oscillometric waveforms were also collected using a computer-based system, processed, and used to derive blood pressure by each of three algorithms: 1. simple fixed-ratio; 2. initial binning according to mean arterial pressure and then application of different fixed ratios within each bin (binning method); and 3. binning plus linear regression to incorporate the influence of additional oscillometric waveform characteristics (binning regression method). The mean of three blood pressure determinations from each algorithm was calculated and compared to the Omron mean, with variability assessed by calculating the standard deviation of the difference compared to the Omron. Results: Mean age in the overall group (± SD) was 65.6 ± 13.7 years (HD 63.4 ± 16.3 years; Stage 3 CKD 65.9 ± 10.7 years), percent female in the overall group was 42% (HD 37%; Stage 3 CKD 47%), and mean arm circumference overall was 31.5 ± 4.7 cm (HD 30.8 ± 4.4 cm; Stage 3 CKD 32.2 ± 5.0 cm. Hypertension was present in 82% of participants (HD 80%; Stage 3 CKD 83%). Mean Omron blood pressure was 136.9 ± 25.4/72.7 ± 15.8 mmHg. Compared to the Omron, the systolic/diastolic results for each algorithm were 134.4 ± 20.5/75.0 ± 12.9 for fixed ratio, 128.5 ± 19.8/71.4 ± 12.0 for binning, and 127.7 ± 18.7/70.2 ± 8.8 for binning regression (Table 1). Conclusions: An oscillometric algorithm based on simple fixed-ratio minimized variability in patients with chronic kidney disease. Manufacturers of devices designed for blood pressure measurement in this population should consider incorporating this approach into their algorithm-based blood pressure derivation.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.028
GPT teacher head0.254
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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