Keeping balance: Author’s reply
Notice bibliographique
Résumé
We thank Dr Müssigbrodt1 for his interest in our study and the complex question of arrhythmogenic right centricular cardiomyopathy (ARVC) diagnosis.2 To continue the discussion in this reply, we elaborate on the elements that he found surprising and rectify inaccuracies in his statements. His first concern regards our choice for an expert panel, instead of ‘reproducible genetic, phenotypic, and clinical criteria’. However, unfortunately, such a ‘gold-standard’ test does not exist for diagnosing ARVC as none of the clinically available tests has sufficient sensitivity or specificity. Relying on genotype would exclude nearly half of cases that are ‘gene-elusive’. Diagnosis must thus result from a composite reference standard: the 2010 Task Force Criteria (TFC).3 There are two recommended evidence-based options to validate composite reference standards; (i) an expert panel or (ii) latent classification learning algorithms,4 of which the latter is suboptimal for relative small cohort sizes in ARVC-research. The appealing strength of an expert panel is that the experts can use the totality of available evidence on an individualized basis, allowing a more comprehensive assessment with multiple shades of grey instead of a limited black-and-white approach from pre-defined criteria with fixed cut-off points. Such expert panels have previously been proven to be valuable in the field of cardiology, for example, to validate the diagnostic value of B-type natriuretic peptide for heart failure.5 Secondly, Dr Müssigbrodt expressed concerns about our control group, a cohort of patients referred for evaluation of ARVC, instead of healthy individuals, and about the disappointing results of several criteria. We consider our control group a major strength of our analysis. In the real world, patients referred for ARVC evaluation have specific traits that resemble ARVC, making them much harder to discriminate. Most of the previously published diagnostic results of the 2010 TFC tests are based on comparison with healthy controls, leading to an overestimation of the true diagnostic accuracy. This explains why several tests, such as signal-averaged electrocardiogram (SAECG), showed lower values in our study. This did however not come as a surprise to us as the recently published expert consensus review of the 2010 TFC advocates for the elimination of SAECG based on ‘non-specific findings and limited diagnostic accuracy’.6 The last point raised by Dr Müssigbrodt, that our statement ‘ECG and arrhythmia criteria alone can rule out ARVC with high sensitivity’ is ‘overconfident’, might be based on a misunderstanding. We fully agree with Dr Müssigbrodt’s statement that ECG and arrhythmia criteria are insufficient to diagnose (i.e. ‘rule-in’) ARVC. Our study rather highlights the importance of the finding of a 100% sensitivity for ‘ruling-out’ ARVC, for example in relatives subject to frequent re-evaluations. As these are two different concepts not to be confused, we want to emphasize the importance of other tests such as cardiac magnetic resonance imaging (CMR) and genetics for diagnosing ARVC. As the first study validating the 2010 TFC as a whole in a real-world population, our results highlight the potential for improvements in ARVC diagnostic criteria. We, like Dr Müssigbrodt, advocate that new diagnostic criteria should maintain a balance between over- and underdiagnoses, and be based on evidence-based approaches. Conflict of interest: none declared.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,042 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,008 | 0,004 |
| Communication savante | 0,009 | 0,006 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,148 | 0,088 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,009 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».