Evaluation of an Educational Program for Clinical Pharmacists to Conduct Standardized Assessments for Medication-Induced Movement-Related Disorders
Notice bibliographique
Résumé
Movement disorders and tardive dyskinesia are common adverse effects of first-generation antipsychotic medications. Although the atypical agents are less likely to result in movement disorders, they are not entirely devoid of this risk. Movement disorders such as akathisia continue to be associated with newer antipsychotics like risperidone and aripiprazole,1,2 and all currently available antipsychotics carry a risk of tardive dyskinesia.3,4 In addition, the anticipated benefits of atypical antipsychotics have been tempered, and they come with their own unique set of adverse effects, factors that together have led to a re-evaluation of the use of conventional antipsychotics.5 Antipsychotic-induced movement disorders and tardive dyskinesia are often underrecognized in clinical practice, with potentially damaging implications for patient care.6 Despite the availability of validated rating scales, evidence of their use remains scarce. Studies have revealed a lack of documentation (both quantity and quality) of movement disorder assessment, as well as challenges associated with accurate detection and management of these adverse effects.7-9 Distinguishing one movement disorder from another continues to be challenging and requires careful evaluation by experienced raters.10 Pharmacists are well positioned to fill this role. Pharmacists build their practice according to a pharmaceut ical care model in which they are held accountable for providing rational drug therapy with the goal of optimizing patient outcomes.11 This goal is achieved by regularly monitoring patient-specific medical data, evaluating the management of medication, and providing pharmaceutical care for the purpose of identifying and resolving drug therapy problems.12,13 Numerous published reports have shown that the provision of clinical pharmacy services, including medication interventions, has resulted in better patient care, shorter hospital stays, and health care cost savings.14 The success of pharmacists in managing diabetes mellitus, lipid abnormalities, anticoagulation, and complex HIV drug regimens has been well documented.15,16 Despite these expanded roles in some settings, pharmacists have been found to represent an underutilized health care resource.17 In the field of mental health, research related to the provision of specific pharmaceutical care services is limited. As such, there is a unique opportunity to study the impact of training pharmacists to assess movement disorders. Pharmacists are in an excellent position to conduct such assessments, as they have regular contact with patients and are experts in medication management, which includes the evaluation and management of adverse effects. At the authors’ clinical site, formal training for such assessments is currently unavailable to pharmacists who routinely work with patients receiving antipsychotic therapy. The value of formal clinician training to better identify antipsychotic-induced movement disorders is emphasized in the literature, including the benefits of having trained pharmacists screen patients for the purpose of identifying and managing these adverse effects.18-20 In this study, investigators developed a new program to train clinical pharmacists to assess medication-related movement disorders. This research functioned as a pilot study, with only a small number of participants, with the intention to provide broader implementation of the program if successful.
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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 | 0,014 | 0,028 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,025 | 0,006 |
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 ».