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Record W4229051401 · doi:10.1016/j.cjcpc.2022.04.002

Using Smartphone Wireless ECG Monitoring to Provide Symptom-Rhythm Correlation in the Paediatric Population

2022· article· en· W4229051401 on OpenAlexaffabout
Alexandra N. Taylor, Andrew E. Warren, Ratika Parkash, Santokh Dhillon

Bibliographic record

VenueCJC Pediatric and Congenital Heart Disease · 2022
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Syncope and Autonomic Disorders
Canadian institutionsQueen Elizabeth II Health Sciences CentreIzaak Walton Killam Health Centre
Fundersnot available
KeywordsPalpitationsPresyncopeMedicineRhythmCorrelationChest painPopulationInternal medicineBlood pressureHeart rate

Abstract

fetched live from OpenAlex

Children frequently present with symptoms of palpitations. These symptoms can occur in isolation or in association with other presentations such as chest pain, presyncope, or syncope. Usually, their symptoms are episodic in occurrence; therefore, establishment of symptom-rhythm correlation is challenging but critical for accurate diagnosis and management. We reviewed the use of smartphone-based wireless electrocardiogram monitoring with AliveCor Kardia, to establish symptom-rhythm correlation in a paediatric case series at a single Canadian tertiary care centre.

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)0.006
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.254
Teacher spread0.239 · 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".

Quick stats

Citations1
Published2022
Admission routes2
Has abstractyes

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