Health professionals’ attitudes toward the detection and management of cancer-related anorexia-cachexia syndrome, and a proposal for standardized assessment
Notice bibliographique
Résumé
BACKGROUND: The identification and management of patients with cancer anorexia-cachexia syndrome (CACS) can be a challenge despite recent international consensus on the definition of the condition. OBJECTIVES: To describe the current views and practice patterns of community oncologists and oncology nurses in regard to CACS and to propose a standardized, pragmatic assessment of CACS for oncological practice. METHODS AND MATERIALS: Responses from 151 community oncologists and nurses obtained across 5 surveys were analyzed. Questions addressed CACS in general and in patients with non-small-cell lung cancer (NSCLC). Surveys 1-3 were directed at physicians, and surveys 4 and 5 were directed at nurses. Surveys 1, 2, 4, and 5 focused on the recognition and monitoring of CACS, and Survey 3 on symptom management. RESULTS: 67% of medical oncologists in Survey 3 selected weight loss as the most important criterion for diagnosing CACS and cited declining appetite and performance status (PS) as the most bothersome effects for patients and families. Weight maintenance/gain was the primary treatment objective for oncologists. Respondents to surveys 1 and 2 acknowledged the risk for CACS is high (60%) in NSCLC but considered the risk much lower (4%) in patients completing a first course of therapy with good PS. 91% of oncologists in Survey 3 reported that symptoms that had an impact on calorie intake were important/very important, and 73% were willing to consider a symptom assessment instrument that included appetite. Nurses in surveys 4 and 5 reported weight loss and appetite were most commonly used to identify cachexia. They considered responsibility for the initial assessment of cachexia was the oncologist's (32%), followed by the nurse practitioner (28%), and the nurse (16%). CONCLUSION: Most oncologists and nurses recognize the core criteria for the CACS, although there may be under-recognition of the condition's prevalence, particularly earlier in the course of treatment. There is considerable interest in adopting a brief assessment tool for screening, management, and referral of patients who are affected by or at-risk of CACS.
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 | 0,005 | 0,000 |
| 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 | 0,001 |
| 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 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 ».