Pain in Patients With Cancer
Notice bibliographique
Résumé
Pain is an important concern in patients with cancer who are receiving active treatment and in long-term cancer survivors. It is one of the most feared aspects of cancer, and it can have a major adverse impact on quality of life. It has long been recognized that untreated or undertreated pain is common in patients with cancer, with little evidence of recent improvement. 1,2 Given the many advances in knowledge regarding cancer pain and its management over the past decade, this Special Series issue was assembled to provide readers with an update on the current understanding of the biology of cancer pain, the biology and mechanisms of action of the opioids used to treat cancer pain, challenges in the management of cancer pain, and evidence-based multidisciplinary approaches to management of cancer pain. We hope that the articles in this Special Series issue will enhance clinician understanding of cancer pain and its treatment and provide practical approaches to managing pain in patients with cancer. Hui and Bruera 3 outline “A Personalized Approach to Assessing and Managing Pain in Patients With Cancer.” They provide a practical yet evidence-based approach to personalized pain assessment and management in the clinic. They also discuss a paradigm shift in pain management, outlining a multistep approach that includes systematic screening, comprehensive pain assessment, characterization of pain, identification of personal modulators of pain expression, documentation of personalized pain goals, and implementation of a multidisciplinary treatment plan with subsequent customized longitudinal monitoring. Their article summarizes an expert clinician’s approach to pain management and provides practical tables and figures that will be helpful to the clinician who manages cancer pain. Falk and Dickenson 4 discuss the biology of pain in patients with cancer, with a special focus on bone pain, in their article entitled “Pain and Nociception: Mechanisms of Cancer-Induced Bone Pain.” The authors review mechanisms of cancer pain, stating that it is a “complex pain state involving components of both inflammatory and neuropathic pain, but also displaying elements that appear to be unique to cancer pain.” They discuss cancer pain as unpredictable, with highly variable intensity, which makes it difficult to manage. They also review mechanisms of acute pain, neuropathic pain, inflammatory pain, and complex cancer pain, providing informative figures that will help clinicians understand the biology of pain and may help guide the selection of management approaches. Gavril Pasternak 5 reviews “Opiate Pharmacology and Relief of Pain,” a complex area that is presented in a clear fashion. Pasternak states that opioids “selectively impact the hurt of nociceptive stimuli”; they act through activation of a pain modulating system consisting of endogenous opioid peptides and their receptors. He discusses three classes of opioid/receptors—mu, delta, and kappa—and suggests that individual responses to specific opioids may reflect differences in the biology of these receptors. Pasternak discusses important issues such as opioid cross tolerance, benefits, and risks of opioid rotation and the growing understanding of mu receptor subtypes, introducing the
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 | 0,003 |
| 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) | 0,000 | 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 ».