Evaluation of an Educational Program for Clinical Pharmacists to Conduct Standardized Assessments for Medication-Induced Movement-Related Disorders
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".