MétaCan
Menu
Retour à la cohorte
Enregistrement W2980594484 · doi:10.1136/bmjebm-2019-ebmlive.100

19 A tale of ill-managed pain and the opioid epidemic

2019· article· en· W2980594484 sur OpenAlex
John S. Mikhaeil, Alexander Huang, Hance Clarke, Marcin Wąsowicz

Pourquoi ce travail est dans la base

Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.

affAu moins un auteur déclare une institution canadienne dans l'instantané OpenAlex épinglé.

Notice bibliographique

RevuePoster presentations · 2019
Typearticle
Langueen
DomaineMedicine
ThématiqueOpioid Use Disorder Treatment
Établissements canadiensToronto General HospitalUniversity Health NetworkUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésOxycodoneMedicineOpioidMedical prescriptionChronic painCancer painHeroinAddictionPsychiatryAlternative medicinePharmacologyDrug

Résumé

récupéré en direct d'OpenAlex

The use of opioids to treat chronic non-cancer pain has increased dramatically in the last decade. Simultaneously, this surge in prescribing practices has been correlated to an almost four-fold increase in opioid-related deaths. Despite recent efforts to control the distribution of opioids and the introduction of abuse-deterrent opioid formulations, the epidemic is still yet to be fully targeted with a multi-factorial approach. In order to better understand how this crisis exists, it is important to examine patterns beginning in the 1980s. At that time, pharmaceutical companies marketed the use of opioids to treat pain and assured clinicians that the addiction profile was low, but these claims were not based on evidence-based medicine. Within a few years, the same companies promoted these drugs for use in long-term non-cancer pain, despite the lack of good evidence. Regardless, this ‘mentality’ was adapted by clinicians based on the pharmaceutical companies’ misrepresentation, which sparked the first of three major waves of increased opioid prescribing. The number of opioid prescriptions increased among primary care clinics and hospitals, and an increased amount of opioid diversion took place. Pharmaceutical companies responded to the public outcry by developing abuse-deterrent formulations such as an extended release oxycodone (OxyContin). These prescribing practices led to increased rates of opioid over-sedation and opioid-related deaths. The second wave began around 2010, as efforts were made to decrease opioid prescribing, leading to the increased popularity of a cheap, widely available option – heroin. Finally, the last resurgence was seen in 2013 due to newer synthetic opioids such as fentanyl. When a patient undergoes a surgical procedure, some level of pain is anticipated post-operatively, and the level of severity and duration varies patient-to-patient. Indirectly related to the illicit opioid epidemic, opioid-prescribing in the postoperative period has increased dramatically, feeding yet another epidemic. Although opioids are often prescribed to address the need for pain management, this is often done without a long-term plan to eventually wean off these medications. As a result, patients are ultimately left with inappropriately managed chronic pain and almost half of these patients continue to use opioids as more of a ‘band-aid’ solution. The issue becomes more complex as these patients seek care from their primary care physicians who may not have experience in weaning patients off opioids. Although some success has been seen with the implementation of prescribing guidelines and educational interventions, new programs need to be implemented to address this systemic issue. This led to the concept of a Transitional Pain Service with a focus on high-risk patients with complex pain management and opioid weaning. This is managed through an interdisciplinary team which is led by anesthesiologists with the goal of minimizing the risk of developing chronic pain postoperatively and long-term opioid use. Through the Transitional Pain Service, patient’s pain management needs are being addressed through a multidisciplinary model and programs are being implemented to combat the growing opioid epidemic that stemmed from a lack of evidence-based medicine.

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.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,016
Score d'incertitude au seuil0,201

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,013
Tête enseignante GPT0,291
Écart entre enseignants0,277 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle