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Using ballistocardiography to measure cardiac performance: a brief review of its history and future significance

2012· review· en· W2064659575 on OpenAlexaff
Emelie Vogt, David S. MacQuarrie, John Neary

Bibliographic record

VenueClinical Physiology and Functional Imaging · 2012
Typereview
Languageen
FieldEngineering
TopicNon-Invasive Vital Sign Monitoring
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsBallistocardiographyMedicineCardiac cycleMeasure (data warehouse)Medical physicsIntensive care medicineCardiologyComputer scienceData mining

Abstract

fetched live from OpenAlex

Ballistocardiography (BCG) is a non-invasive technology that has been used to record ultra-low-frequency vibrations of the heart allowing for the measurement of cardiac cycle events including timing and amplitudes of contraction. Recent developments in BCG have made this technology simple to use, as well as time- and cost-efficient in comparison with other more complicated and invasive techniques used to evaluate cardiac performance. Recent technological advances are considerably greater since the advent of microprocessors and laptop computers. Along with the history of BCG, this paper reviews the present and future potential benefits of using BCG to measure cardiac cycle events and its application to clinical and applied research.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.003
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.098
GPT teacher head0.322
Teacher spread0.224 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations52
Published2012
Admission routes1
Has abstractyes

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