Patient reported symptoms after cancer diagnosis and the risk of short- and long-term severe mental health events among adolescents and young adults (AYA): A population-based study.
Notice bibliographique
Résumé
12019 Background: AYA with cancer are a vulnerable sub-population at risk of adverse mental health outcomes during and after cancer treatment. Tools to identify AYA at highest risk are required to guide screening and interventions. In a population-based cohort of AYA with cancer, we determined whether self-reported symptoms were associated with subsequent short- and long-term severe mental health events (SMHE). Methods: All Ontario, Canada AYA diagnosed with cancer aged 15-29 between 2010-2018 were identified and linked to healthcare databases, including one capturing self-reported Edmonton Symptom Assessment System (ESAS) scores at cancer-related visits. Scores for depression, anxiety, and poor well-being were categorized as not measured, mild (0-3), moderate (4-6), or severe (7-9). SMHE were defined as emergency room visits or hospitalizations for mental health reasons. First, we used Cox proportional hazard models to determine the association of ESAS scores (time-varying variable) with subsequent SMHE. Second, among 5-year survivors, we determined the association of maximum ESAS score within the first year of diagnosis with long-term SMHE (i.e. starting at 5 years from cancer diagnosis). All analyses were adjusted for patient and disease variables, including mental healthcare use prior to cancer diagnosis. Results: 5,435 AYA met inclusion criteria. Median age at cancer diagnosis was 25 years [interquartile range 22-27]. Hematologic cancers were most common (1,748; 32.2%). Symptom severity was associated with subsequent SMHE risk. For example, AYA reporting severe anxiety were at more than three-fold higher risk of SMHE compared to those reporting mild anxiety [adjusted hazard ratio (aHR) 3.6, 95th confidence interval (CI) 1.9-6.7; p < 0.001]. Similar risk was seen among AYA reporting severe vs. mild depression (aHR 3.5, 1.7-7.3; p < 0.001). Among 3,518 (64.7%) 5-year survivors, symptom severity also predicted long-term SMHE. For example, starting at 5 years post cancer diagnosis, the subsequent 3-year cumulative incidence of a SMHE among those who reported severe depression at any time during the first year post cancer diagnosis was 10.5% (95CI 6.9-15.9) compared to 2.4% (95CI 1.7-3.3) among those who only reported mild depression (aHR 3.0, 95CI 1.8-4.9; p < 0.0001). Similar results were seen pertaining to severe anxiety and severe impact on well-being. AYA endorsing severe anxiety represented 13.1% of the cohort but accounted for 25.8% of AYA experiencing SMHEs during the first three years of survivorship. Conclusions: Systematic symptom screening in the first year after cancer diagnosis identifies a proportion of AYA at high risk of both short and long-term SMHE who may benefit from targeted screening and interventions. Future work will determine whether interventions during cancer treatment mitigate this risk.
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 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 ».