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Record W2148457163 · doi:10.1586/14779072.4.5.681

Minimally invasive approach to the child with palpitations

2006· review· en· W2148457163 on OpenAlexaff
Kesava Rajagopalan, James E. Potts, Shubhayan Sanatani

Bibliographic record

VenueExpert Review of Cardiovascular Therapy · 2006
Typereview
Languageen
FieldMedicine
TopicCardiac Arrhythmias and Treatments
Canadian institutionsBC Children's HospitalUniversity of British ColumbiaMedtronic (Canada)
Fundersnot available
KeywordsPalpitationsMedicineReferralIntensive care medicineSudden cardiac deathEtiologyPhysical examinationHeart diseasePediatricsMedical emergencyCardiologyInternal medicineFamily medicine

Abstract

fetched live from OpenAlex

Palpitations are a common reason for referral to a pediatric cardiologist. Although generally benign, palpitations are a significant cause for concern in the individual and their family. Similarly, palpitations may be the initial presentation of significant heart disease, resulting in heightened concern in the referring physician. Although emphasis is usually placed on excluding arrhythmia as the cause for palpitations, there are a variety of noncardiac causes for palpitations. The patient history and physical examination are the key components of the evaluation and guide subsequent investigations. In many cases, an immediate diagnosis cannot be made and additional testing may be required; this often includes further monitoring for episodes, cardiac imaging and ambulatory monitoring. Current technologies for ambulatory monitoring during symptoms include Holter monitoring and a variety of patient-activated event recorders, including implantable loop recorders. Each presents its own unique advantages and disadvantages to aid diagnosis in the management of a child with palpitations. The primary focus for the clinician is to determine whether the etiology is benign in nature or whether there is underlying heart disease that may carry a more serious prognosis.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.941
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.007
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 head0.029
GPT teacher head0.319
Teacher spread0.290 · 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.

Study designOther design
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

Citations12
Published2006
Admission routes1
Has abstractyes

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