Determinants of analgesic responses following medical cannabis initiation among patients with chronic pain
Notice bibliographique
Résumé
Background: Pain is one of the most common reasons for which patients visit healthcare providers. In Canada, chronic pain is a major health problem that affects about 20% of adults. Over the past two decades, there has been a rise in the use of cannabis for therapeutic purposes, and there is a growing body of research supporting the analgesic benefits of medical cannabis for patients with chronic pain. To date, however, little is known on the factors that contribute to the analgesic benefits of cannabis in these patients. Given that symptoms of anxiety and depression (i.e., negative affect) and sleep problems are linked to heightened clinical pain intensity, there is reason to believe that reductions in patients' negative affect and sleep problems resulting from cannabis use might indirectly contribute to reductions in pain intensity. It is possible that other patient-specific factors might also contribute to the analgesic benefits of medical cannabis, but these factors have remained largely unexplored. Objectives: The first objective of the present thesis was to examine if medical cannabis use was associated with reductions in “average” pain as well as with clinically significant (i.e., ≥ 30%) reductions in clinical pain intensity among patients with chronic noncancer pain. The second objective of the present thesis was to examine whether the association between medical cannabis use and reductions in clinical pain intensity was attributable to concurrent changes in negative affect and pain-related sleep interference. The third objective was to examine whether patient-specific characteristics were associated with reductions in clinical pain intensity following initiation of medical cannabis.Methods: In this longitudinal study, chronic noncancer pain patients (n = 2068) completed self-report measures assessing a host of sociodemographic, lifestyle, medical, and psychological variables. Measures assessing clinical pain intensity, negative affect, and pain-related sleep interference were completed at baseline and every three months, for a duration of one year, following initiation of medical cannabis. Results: Analyses first indicated that initiation of medical cannabis was associated with reductions in “average” pain intensity (p < .05). However, medical cannabis use was not significantly associated with clinically significant (i.e., ≥ 30%) reductions in pain intensity. Further analyses indicated that initiation of medical cannabis was associated with reductions in negative affect and pain-related sleep interference (both p's < .05). Interestingly, reductions in pain intensity following the initiation of medical cannabis remained significant even after controlling for concurrent reductions in negative affect and pain-related sleep interference (p < .05). Analyses subsequently showed that patient characteristics such as age and sex were significant predictors of reductions in average pain intensity following initiation of medical cannabis (both p's < .05). Conclusion: Findings from this thesis provide valuable new insights into our understanding of factors that may contribute to reductions in pain among patients with chronic pain who are using medical cannabis. Our results suggest that reductions in pain intensity following medical cannabis initiation are likely to be explained, in part, by concurrent reductions in negative affect and pain-related sleep interference. However, our findings indicated that reductions in pain following cannabis use cannot be entirely attributable to concurrent changes (i.e., reductions) in these variables. Finally, our findings indicated that the association between medical cannabis use and reductions in pain intensity was more pronounced among certain subgroups of patients, such as women and older patients. From a clinical point of view, our results could have implications for clinicians involved in the management of patients who might be considering medical cannabis as a therapeutic avenue
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 | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 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) | 0,002 | 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 ».