A Case Control Study Examining the Patterns and Predictors of Referral to Cancer Rehabilitation at Canada's Largest Comprehensive Cancer Centre
Notice bibliographique
Résumé
BACKGROUND: Cancer rehabilitation has become increasingly relevant as the number of cancer survivors grows, coupled with the high-documented rates of adverse effects and related disability. Cancer rehabilitation can reduce functional limitations among cancer survivors and enhance their well-being. However, only a small proportion of individuals are referred to rehabilitation services. To identify and address disparities and foster access, it is essential to develop a better understanding of the factors that drive referral to cancer rehabilitation services. METHODS: The purpose of this study was to: (1) describe the sociodemographic and clinical characteristics and symptom burden of patients who were referred to the Princess Margaret Cancer Rehabilitation and Survivorship (CRS) Program between 2017 and 2019 and (2) Compare these variables between patients who were referred to CRS (n = 2783) and matched cases who were not referred over this period (n = 18,434). A retrospective secondary analysis of data extracted from the Princess Margaret (PM) Cancer Registry, electronic patient records, and patient-reported outcome data (PROMs) (including ESAS-r and ECOG status) was performed. Summary statistics were used to describe the patients referred to the CRS program. Multivariable logistic regression modelling was used to identify factors associated with likelihood of referral. RESULTS: Most referred patients were female (74%), English speakers (93%) and half lived within 15 km of the referred hospital. The most common reasons for referral were musculoskeletal impairment (26%) and lymphedema (25.4%). Many patients (45%) had multiple reasons for referral. Several key predictors of referral were identified including closer distance to hospital, lower age (< 65 years), cancer site, and completion of PROMs. For those who completed PROMs, patient reported function status and pain scores were related to referral. CONCLUSION: The findings can be helpful in optimizing the referral processes and addressing disparities regarding access to cancer rehabilitation. Solutions are likely multifaceted including health care provider and patient education and systemic changes to address barriers.
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) | 0,001 | 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 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 ».