Type-D Personality and Heart Disease: It Might Be ‘One Small Step’, but It Is Still Moving Forward: A Comment on Grande et al.
Notice bibliographique
Résumé
Since the landmark studies of the late 60s and early 70s, we as a field have tried to find the key psychosocial predictors of poorer cardiovascular outcomes. In the mid-90s, a new construct, type-D personality, was found to have significant predictive ability for mortality in patients with coronary heart disease [1]. This has led to a constant stream of original studies and reviews exploring the potential role of type-D personality in the progression of heart disease. Of particular note, there has been a recent spate of systematic reviews [2–4] on the topic. In spite of this, the review by Grande et al. [5] truly provides an extension, not only through the inclusion of new studies, but also in its conceptualisation. Grande and colleagues have taken a rigorous and refined approach to assessing the impact of type-D personality on cardiac outcomes, which has created a number of interesting talking points. One particularly interesting point rightly highlighted by the authors is the contrasting data on patients with coronary artery disease vs. those with congestive heart failure (CHF). They raise the issue of whether this difference is driven by a true lack of prognostic effects in CHF patients or if this just reflects the increased sample size in the newer larger CHF studies and tend, with good reason, to lean more on the side of an actual prognostic difference. There is, however, an alternative possibility which revolves around the appropriate cut point for different populations. The type-D scale, which was used in 11 out of the 12 studies, was developed in a specific population, a Flemish/Dutch coronary artery disease population [6], and whilst there has been some good work to establish the factor structure invariance of the scale across different languages and populations [7], the same cannot be said for the cut point of 10. One of the main underpinnings of good psychometrics is that all aspects of a scale or questionnaire are validated when used in new populations, i.e. any population that differs substantially from the original cohort the scale/questionnaire was developed in. Following this, it could be that type-D personality may be predictive of outcomes in CHF populations, but not with a cut point of 10. Further complicating this issue is the way in which the original cut point was generated. The figure of 10 is derived from a median split of negative affect and social inhibition in the original validation study. We think all of us would argue that this is not the optimal way of defining a diagnostic level, though, as highlighted by Grande and colleagues, what is the comparator or ‘reference standard’ [8] needed to determine the classification accuracy of the type-D cut point? Suffice to say, there needs to be more work around the optimisation of a type-D cut point, or if no valid measure can be reliably generated, then only continuous data should be used in the future.
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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 | 0,027 | 0,133 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,005 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,004 | 0,007 |
| Communication savante | 0,006 | 0,017 |
| Science ouverte | 0,010 | 0,005 |
| Intégrité de la recherche | 0,056 | 0,070 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,007 |
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 ».