Benefits, Harms, and Stakeholder Perspectives Regarding Opioid Therapy for Pain in Individuals With Metastatic Cancer: Protocol for a Descriptive Cohort Study
Notice bibliographique
Résumé
BACKGROUND: Opioids are a key component of pain management among patients with metastatic cancer pain. However, the evidence base available to guide opioid-related decision-making in individuals with advanced cancer is limited. Patients with advanced cancer or cancer that is unlikely to be cured frequently experience pain. Opioids are a key component of pain management among patients with metastatic cancer pain. Many individuals with advanced cancer are now living long enough to experience opioid-related harm. Emerging evidence from chronic noncancer pain literature suggests that longer-term opioid therapy may have limited benefits for pain and function, and opioid-related harms are also a major concern. However, whether these benefits and harms of opioids apply to patients with cancer-related pain is unknown. OBJECTIVE: This manuscript outlines the protocol for the "Opioid Therapy for Pain in Individuals With Metastatic Cancer: The Benefits, Harms, and Stakeholder Perspectives (BEST) Study." The study aims to better understand opioid decision-making in patients with advanced cancer, along with opioid benefits and harms, through prospective examination of patients' pain experiences and opioid side effects and understanding the decision-making by patients, care partners, and clinicians. METHODS: This is a multicenter, prospective cohort study that aims to enroll 630 patients with advanced cancer, 20 care partners, and 20 clinicians (670 total participants). Patient participants must have an advanced solid cancer diagnosis, defined by the American Cancer Society as cancer that is unlikely to be cured. We will recruit patient participants within 12 weeks after diagnosis so that we can understand opioid benefits, harms, and perspectives on opioid decision-making throughout the course of their advanced cancer (up to 2 years). We will also specifically elicit information regarding long-term opioid use (ie, opioids for ≥90 consecutive days) and exclude patients on long-term opioid therapy before an advanced cancer diagnosis. Lived-experience perspectives related to opioid use in those with advanced cancer will be captured by qualitative interviews with a subset of patients, clinicians, and care partners. Our data collection will be grounded in a behavioral decision research approach that will allow us to develop future interventions to inform opioid-related decision-making for patients with metastatic cancer. RESULTS: Data collection began in October 2022 and is anticipated to end by November 2024. CONCLUSIONS: Upon successful execution of our study protocol, we anticipate the development of a comprehensive evidence base on opioid therapy in individuals with advanced cancer guided by the behavioral decision research framework. The information gained from this study will be used to guide interventions to facilitate opioid decisions among patients, clinicians, and care partners. Given the limited evidence base about opioid therapy in people with cancer, we envision this study will have significant real-world implications for cancer-related pain management and opioid-related clinical decision-making. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/54953.
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 | 0,060 | 0,050 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,003 |
| Méta-épidémiologie (sens large) | 0,004 | 0,006 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,006 | 0,003 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,004 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,042 | 0,007 |
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 ».