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Record W2803973046 · doi:10.4103/digm.digm_2_18

Pulse wave analysis for cardiovascular disease diagnosis

2018· article· en· W2803973046 on OpenAlexaff
Jingjing Xia, Simon Liao

Bibliographic record

VenueDigital Medicine · 2018
Typearticle
Languageen
FieldEngineering
TopicNon-Invasive Vital Sign Monitoring
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsMedicinePulse Wave AnalysisPulse (music)DiseasePulse waveCardiologyInternal medicinePulse wave velocityPhysical therapyBlood pressureComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

ABSTRACT Background: In this research, the pulse wave data of 274 subjects from both the patient and control groups are evaluated and analyzed. Based on the pulse wave analysis of those subjects, a study of diagnosing cardiovascular diseases is conducted. Methods and Results: By investigating the correlation between the cardiac indices Reverse Shoulder Index (RSI) and Ratio of Distance for patients with cardiovascular diseases from different age and gender groups, several common and important observations are reported. By carrying out case studies, we have verified some of our findings with several patient cases. Conclusion: In this research, pulse wave analysis is applied for the study of cardiovascular diseases with some important observations. We expect that our discoveries in this research can eventually help the end-users in cardiovascular diseases diagnosis.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.497
Threshold uncertainty score0.686

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.233
Teacher spread0.207 · 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 teacher head, 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".

Quick stats

Citations7
Published2018
Admission routes1
Has abstractyes

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