Comment on: Prevalence, Risk Factors and Assessment of Depressive Symptoms in Patients With Systemic Sclerosis
Notice bibliographique
Résumé
Dr. March et al.[1] administered the Major Depression Inventory (MDI) to 94 systemic sclerosis (SSc) patients and reported that “the prevalence of depressive symptoms” based on MDI scores of ≥20 was 22.3%, which they described as “high prevalence”. Self-report symptom questionnaires like the MDI, however, are not designed to ascertain case status or estimate prevalence and should not be used for this purpose. Members of our team published studies in 2007-2008 that used questionnaires for this purpose.[2,3] However, since then we have demonstrated that depression symptom questionnaires tend to overestimate prevalence, sometimes substantially.[4,5] This is because cutoffs on depression screening questionnaires are typically set to cast a wide net and identify a pool of people who may have depression - but not to ascertain case status. The degree to which estimates of prevalence generated from questionnaires may overestimate depression depends on the questionnaire and cutoff used. Nonetheless, as an example, for the commonly used nine-item Patient Health Questionnaire (PHQ-9) and a standard cutoff of ≥10, sensitivity and specificity are 88% and 85%, respectively.[6] Thus, a “prevalence” of 15% would be generated even if there are no participants with depression. Illustrating this problem further in SSc, Jewett et al.[7] reported that the 30-day prevalence of major depressive disorder among 345 SSc patients based on a validated diagnostic interview was 3.8%. However, based on the PHQ-9, which was administered simultaneously, and a cutoff of ≥10, the rate was 27%,[6] more than seven times the actual prevalence. The MDI has been used mostly among patients with depressive disorders or those suspected of having depression, and no large primary studies or systematic reviews have established its accuracy for screening or identifying case status among non-psychiatric populations, as used in the study by Dr. March et al.[1] Thus, it is not known how the percentage of participants with scores of 20 or greater would relate to the percentage who might have a depressive disorder. Labeling the percentage of patients who score above a threshold on a self-report questionnaire as “prevalence of depressive symptoms” rather than depression does not solve the problem. Labeling this as prevalence still clearly indicates that there is some entity that exists and begins at that threshold. However, there is no evidence showing that any cutoff on the MDI separates people into those with significant impairment and those without, which is the purpose of diagnosis. Second, if the objective is simply to identify a threshold where symptoms are present and greater than those below the threshold, any cutoff could be used, rendering any given threshold meaningless in terms of “prevalence”. It is likely the case that people with SSc are more likely to have depression than people without the disease. The percentage reported in the study by Dr. March et al.,[1] however, does not allow us to draw conclusions about the degree that this may be the case.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».