Abstract 1830: Factors associated with fatigue, sleep dysfunction, and joint symptoms in breast cancer survivors
Notice bibliographique
Résumé
Abstract Background: Fatigue, sleep dysfunction, and joint symptoms may negatively impact quality of life in breast cancer survivors (BCS). Prior studies examining associated factors have yielded inconsistent results. Objective: Determine demographic, medical, exercise-related, body composition and serum inflammatory factors associated with fatigue, sleep dysfunction, and/or joint symptoms in BCS. Methods: Post-hoc analysis of baseline data completed by 37 BCS enrolled in a pre/post intervention study or randomized trial. Self administered survey assessed demographics, medical variables, self-efficacy (i.e., confidence in ability to exercise), fatigue (FSI), sleep dysfunction (PSQI), and joint symptoms (WOMAC). Sleep was also objectively assessed with an accelerometer. Body composition was assessed by body mass index (BMI), waist-to-hip ratio (WHR), and percent body fat (bioelectrical impedance). Cardio respiratory fitness was assessed with sub maximal treadmill test and muscle strength with back/leg extensor dynamometer. Multiplex high sensitivity assay or ELISA was performed on fasting serum samples to determine levels of cytokines or other markers related to body composition. Due to the skewed nature of several variables, Spearman correlations (r) were performed. A significant p value was set at <.05. Results: Participant mean age and education were 55 ± 10.1 and 15 ± 2.8 years, respectively. The majority (87%) were of European heritage with breast cancer stage distribution being I (51%), II (38%) or III (11%). Daily amount of fatigue was significantly associated with months since chemotherapy (r=.43, p = .027), number of comorbidities (r = .33, p = .0497), and fitness (r = -.35, p = .033). Depending on the subscale, self reported sleep dysfunction was associated with income (r = -.36, p = .029), number of comorbidities (r = .37, p = .028), WHR (r = .45, p = .005), tumor necrosis factor (TNF) α (r = .36, p = .029), interleukin (IL)-8 (r = -.45, p = .006), IL-10 (r = -.42, p = .011), TNF α /IL-10 ratio (r = .54, p = <.001), insulin (r = .48, p = .003), and adiponectin (r = -.36, p = .031). Time awake while in bed (accelerometer) was significantly associated with race (r = -.39, p = .019), muscle strength (r = -.35, p = .034), fitness (r = -.48, p = .003), adiponectin (r = -.39, p = .020), and monocytic chemotactic protein (MCP)-1 (r = -.33, p = .047). Time asleep was associated with race (r = .36, p = .031) and leptin (r = -.36, p = .031). Joint symptoms were significantly associated with age (r = .43, p = .008), education (r = -.34, p = .041), number of comorbidities (r = .60, p = <.001), self-efficacy (r = -.55, p = .003) and BMI (r = .33, p = .048). Conclusion: Fatigue and sleep dysfunction were significantly associated with medical factors and fitness. Sleep dysfunction was also associated with demographics, inflammation, strength, and body composition. Joint symptoms were associated with demographics, medical factors, self-efficacy and BMI. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 1830. doi:10.1158/1538-7445.AM2011-1830
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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 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) | 0,004 | 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 ».