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Record W4206507503 · doi:10.3389/fphar.2021.793361

Impact of a Collaborative Pharmaceutical Care Service for Patients With Parkinson’s Disease

2022· article· en· W4206507503 on OpenAlexaff
Zhan‐Miao Yi, Sarah Willis, Yuan Zhang, Na Liu, Qiyu Tang

Bibliographic record

VenueFrontiers in Pharmacology · 2022
Typearticle
Languageen
FieldMedicine
TopicMedication Adherence and Compliance
Canadian institutionsMcMaster UniversityImpact
FundersPeking University Third HospitalPeking UniversityNational Natural Science Foundation of China
KeywordsMedicinePsychological interventionPharmacistWilcoxon signed-rank testPharmaceutical careAdverse effectInternal medicineQuality of life (healthcare)DiseasePhysical therapyPharmacyMann–Whitney U testFamily medicine

Abstract

fetched live from OpenAlex

Objective: To identify the impact of a collaborative pharmaceutical care service (CPCS) on medication safety and establish the impact of the CPCS on patient reported outcomes for Parkinson’s disease (PD) patients. Methods: Initially, PD outpatients receiving the CPCS between March 2017 and March 2019 were compared with PD patients receiving standard of care to identify differences in management. Pharmacist interventions data were coded and patients with PD receiving the CPCS were compared with those receiving standard of care to determine differences in medicines prescribed and dosage associated with these. Following this, data of patients receiving CPCS at baseline and 3-months follow-up were collected using a questionnaire consisting of validated measures of two patient-reported outcomes [adherence and quality of life (QoL)]. Mean scores for continuous variables were calculated, with descriptive analysis of categorical variables consisting of frequency counts and percentages. Change in adherence score before and after CPCS was investigated using a Wilcoxon sign rank sum test, spearman correlation analysis was used to correlate the changes in QoL before and after CPCS with the number of interventions, and p < 0.05 indicates that the difference is statistically significant. Results: A total of 331 PD outpatients received CPCS over 490 outpatient visits with an average age of 71.83 (±12.54). Five hundred and forty-five drug related problems were recorded as pharmacist interventions, of which most involved change to dosage ( n = 226, 41.47%), adverse drug reactions ( n = 135, 24.77%), and change in a medication ( n = 102, 18.72%). Compared with those receiving standard of care, patients receiving CPCS were significantly less likely to have been prescribed pramipexole (18.52 versus 23.77%, p < 0.001) and more likely to have been prescribed amantadine (5.40 versus 3.70%, p = 0.02) and selegiline (17.36 versus 11.64%, p < 0.001). Lower dosages of levodopa/benserazide (0.51 ± 0.31 g versus 0.84 ± 0.37 g, p < 0.001), levodopa/carbidopa (0.33 ± 0.23 g versus 0.66 ± 0.47 g, p < 0.001), pramipexole (1.14 ± 1.63 mg versus 1.27 ± 0.69 mg, p = 0.01), and entacapone (130.00 ± 79.76 mg versus 173.09 ± 97.86 mg, p < 0.001) were also recorded. At baseline 119 PD outpatients with an average age of 69.98 (±9.90) were recruited for the longitudinal study. At 3-month follow-up, participants reported improvement in bodily pain subscale (baseline versus 3-months follow-up, 30.04 ± 22.21 versus 23.01 ± 20.98, p = 0.037) and medication adherence (6.19 ± 1.50 versus 6.72 ± 1.73, p = 0.014). Frequency of CPCS use was related to activity of daily living subscale ( p = 0.047), the bodily pain subscale ( p = 0.026), and medication adherence ( p = 0.011). Total score of PDQ-39 was associated with patient education ( p = 0.005) and usage and dosage combined with patient education ( p = 0.006), while medication adherence score was associated with usage and dosage ( p = 0.005). Conclusion: The CPCS was effective in resolving drug-related problems and in improving patients’ medication regimens, medication adherence, and QoL through patient education and dosage adjustments. This is the first step in the development and feasibility testing of pharmacy services for PD patients in China.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.108
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.017
GPT teacher head0.352
Teacher spread0.336 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2022
Admission routes1
Has abstractyes

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