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Record W4387746102 · doi:10.1016/j.cjcpc.2023.10.007

The Role of Cardiopulmonary Testing to Risk Stratify Tetralogy of Fallot Patients

2023· article· en· W4387746102 on OpenAlexaff
Benedetta Leonardi, Barbara Cifra

Bibliographic record

VenueCJC Pediatric and Congenital Heart Disease · 2023
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsHospital for Sick Children
FundersMinistero della Salute
KeywordsTetralogy of FallotMedicineCardiologyInternal medicineIntensive care medicineHeart disease

Abstract

fetched live from OpenAlex

Neonatal repair has completely changed the clinical history of patients with tetralogy of Fallot (ToF); however, these patients carry a significant risk of severe arrhythmias and sudden cardiac death in the long term. The exact mechanism for late sudden cardiac death is multifactorial and still not well defined, and the risk stratification for primary prophylaxis in these patients remains challenging. Cardiopulmonary exercise testing (CPET) is a well-established and safe method to assess cardiopulmonary function in children and adults with congenital heart disease. Several parameters obtained with CPET have been identified as potential prognostic of major adverse cardiovascular events in congenital heart disease. CPET is routinely used to assess functional capacity also in patients with ToF, and there is some evidence showing its usefulness in predicting the cardiac adverse events in patients with repaired ToF. Current guidelines recognize the importance of CPET in the evaluation and management of patients with ToF, but there is no clear consensus on which the CPET parameter or level of exercise intolerance, as measured by CPET, is truly predictive of an increased risk of arrhythmia and major adverse cardiovascular events in this population. Therefore, the aim of this narrative review is to describe the current evidence on the potential use of CPET in the risk stratification of patients with repaired ToF.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.030
Threshold uncertainty score0.580

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.013
GPT teacher head0.253
Teacher spread0.240 · 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

Citations12
Published2023
Admission routes1
Has abstractyes

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