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Record W2168031938 · doi:10.1186/1532-429x-14-s1-o1

MRI in childhood Arrhythmogenic Right Ventricular Cardiomyopathy and proposed modification of the Task Force Criteria for children

2012· article· en· W2168031938 on OpenAlexaff
Lars Grosse‐Wortmann, Yousef Etoom, Sindu Govindapillai, Brian W. McCrindle, Cedric Manlhiot, Shi‐Joon Yoo

Bibliographic record

VenueJournal of Cardiovascular Magnetic Resonance · 2012
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Effects of Exercise
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMedicineAngiologyTask forceMagnetic resonance imagingCardiomyopathyCardiologyInternal medicineArrhythmogenic right ventricular dysplasiaRadiologyHeart failure

Abstract

fetched live from OpenAlex

ARVC is a genetically determined cardiomyopathy which typically manifests clinically during the second to fourth decade of life. The diagnosis is made using a scoring system of signs and symptoms, known as the revised task force criteria (rTFC). MRI has recently been shown to be of little added value in the diagnosis of ARVC in adults. Its role in the pediatric age group is unclear. We sought assess the usefulness of magnetic resonance imaging (MRI) in the diagnosis of arrhythmogenic right ventricular cardiomyopathy (ARVC). We retrospectively reviewed the MRI studies of all pediatric patients who were referred to MRI for signs of ARVC between 2005 and 2009. Following exclusion of serial studies in the same patient and those with poor image quality, 145 studies were analyzed for wall motion abnormalities (WMA), fibrofatty infiltration, and right ventricular (RV) volume. A diagnosis of possible, borderline, or definitive ARVC was made on the basis of the rTFC. Figure 1 shows the reasons for referral. 39% of the patients were unaffected, 27% had possible, 21% borderline, and 13% definitive ARVC. Fatty infiltration and myocardial fibrosis were detected in only 1 and 3 patients, respectively, all of whom had severe WMA. WMA severity correlated with the certainty of the ARVC diagnosis. A c-analysis revealed that the accuracy of the rTFC did not suffer from removing the echo- and electrocardiograms from the diagnostic work-up. On the contrary, removing the family history or the MRI grossly reduced the diagnostic performance of the rTFC. This is in stark contrast to the findings in adults (Figure 2 ). "Non-rTFC" such as RV thinning, RV outflow tract dilatation, and abnormal trabeculations had a low sensitivity, but high specificity for ARVC. Patients with definitive ARVC had significantly larger left ventricles than those without, possible or borderline ARVC (90ml/m2 vs. 88,89,90ml/m2, respectively). Reasons for referral for MRI to assess for signs of ARVC C-Analysis from the paper by (A) Marcus and (B) of our data. The larger the columns the more important the criterion is for the diagnosis of ARVC. MRI and family history are the highest performing criteria in our cohort as opposed to the adult population where echo is most and MRI least important. Unlike in adults, MRI is a useful and important tool in the diagnostic work-up of ARVC in children and adolescents. The reason lies within the more subtle degree of WMA in this age group which are not detected by echocardiography but found on MRI. In the pediatric age group, fibrofatty degeneration is found rarely and never without wall motion abnormalities. The respective sequences should be omitted in this population. Our data strengthen the concept that ARVC is a global, biventricular disease, rather than an isolated RV cardiomyopathy. Based on our data, we are proposing a modification of the rTFC to exclude certain criteria and incorporate non-rTFC MRI findings, leading to a novel scoring system for pediatric ARVC.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.006
GPT teacher head0.229
Teacher spread0.223 · 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

Citations2
Published2012
Admission routes1
Has abstractyes

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