Use of an Abbreviated Geriatric Screening Tool in the Assessment of Older Cancer Patients’ Functional Status, Dependency, and Comorbidities: Cross-Sectional Audit and Observations From a Regional Cancer Center in Australia
Notice bibliographique
Résumé
BACKGROUND: Malignancies are the leading cause of disease burden in Australia, comprising 19% of total diseases. Approximately 1 in 4 men and 1 in 6 women die from malignancies by 85 years of age, with patients aged 65 years and older contributing to 58% of diagnoses and 76% of cancer mortality. In the context of malignancy-related disease and age-related degeneration, there is a need for comprehensive assessment of older patients to plan for appropriate management and predict prognosis. The utility of available comprehensive geriatric assessment tools has been limited in routine practice because of their time-consuming nature, despite their informing clearer understanding of patients' functional status, better clinical decision making, prevention of unpredictable admissions and emergency department overload, and support services planning. Though there are several promising tools available, there is a lack of literature on tools that can comprehensively assess functional status in an expedited fashion. OBJECTIVE: This study aimed to document functional status and comorbidities among a geriatric oncology patient cohort attending a regionally located, dedicated cancer care facility, using the completed Adelaide tool assessments. This study documents cohort characteristics, including sociodemographics, malignancy type, and comorbidities. Secondarily, we observed the utility of an abridged functional assessment in the multidisciplinary team (MDT) management of older cancer patients. METHODS: The study comprised a facility-based cross-sectional audit of results obtained from a screening tool administered to patients aged 65 years and older and attending an outpatient medical oncology clinic for management of cancer from late 2015 to 2017. Data relating to five domains were collected, including instrumental activities of daily living, activities of daily living, performance status, unintended weight loss, and exhaustion. Sociodemographic and disease-related factors were summarized as frequencies with percentages or mean with SD. Distribution of functional status based on sociodemographic characteristics, living status, disease-related factors, and comorbidities was analyzed using a chi-square test. Cumulative dependencies in the five domains were identified, and patients were classified as fit, vulnerable, or frail. Supplementary review of presentation notes for cases discussed at MDT meetings was undertaken to identify discrepancies. RESULTS: A majority of the study population showed poor functional status, with 88.7% (243/274) categorized as vulnerable and 8.4% (23/274) as frail. Exhaustion and unintended weight loss were identified as the most common contributors to dependency. Polypharmacy was strongly associated with decreased functional status. CONCLUSIONS: The outcomes of this study are congruent with the existence of dependency in various domains, and with similar research in geriatric oncology. The Adelaide tool provided a useful basis for MDT discussion and management, where cases were referred to the MDT. We recommend further examination of the tool's utility and impact in clinical decision making, and the distribution of dependencies in a rural cohort compared with metropolitan patients.
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 | 0,002 | 0,005 |
| 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 | 0,001 | 0,001 |
| 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 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 ».