Predictors of depression in older adults with multiple sclerosis
Notice bibliographique
Résumé
Canada’s growing population of older people with MS (PwMS) has warranted a closer look into factors associated with depression that may interfere with healthy aging. At the same time, researchers, clinicians, and medical professionals treating PwMS with mood disorders have spoken about the difficulty they face when having to determine a diagnosis of depression in this population. This difficulty arises because both MS and depression share various psychological and neurological symptoms (e.g., fatigue, pain, sleep difficulties, psychomotor retardation, and cognitive difficulties). It is also found that these overlapped symptoms vary when completing a self-report measure of depression, versus when medically diagnosed by a psychiatrist. As such, we aim to investigate the personal and disease-related factors that are associated with self-reported depressive symptoms and medically diagnosed depression (i.e. depression diagnosed by a medical professional). Following this, we aim to determine the risk factors for depression in older PwMS. This study used secondary data collected from the original study, the Canadian survey of health, lifestyle, and aging with multiple sclerosis. Data of the original study was collected from 743 Canadians (> 55 years of age and living with MS for >20 years). In this present study, presence of self-reported depressive symptoms was defined as a score of ≥ 8 on the depression component of the Hospital Anxiety and Depression Scale (HADS-D). Presence of medically diagnosed depression was determined by the item that asked participants if they have received a diagnosis of depression by their medical professional. Logistic regression was used to identify variables that predicted depression. Self-reported depressive symptoms were found in 30.5% of the population, while medically diagnosed depression was found in 25.7%. 11.7% of PwMS had both self-identified depressive symptoms and were diagnosed with depression by their medical professional. Low social support, high perceived disability, and additional comorbid physical conditions were independent predictors of depression in older PwMS in our cohort. Depression is prevalent in older PwMS. Clinicians should be cognizant of the overlap of symptoms between MS and depression and should employ possible ways to minimize over-diagnosing or underdiagnosing depression in this population. Identifying risk factors for depression is imperative because at-risk individuals may be thoroughly assessed for depression and will be able to receive treatment more promptly.
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 | 0,000 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 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 ».