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Record W3020340013 · doi:10.4140/tcp.n.2020.230

Impact of Pharmacist Interventions in an Ambulatory Geriatric Care Clinic: The IMPACC Study

2020· article· en· W3020340013 on OpenAlexaboutno aff
Patrick Viet-Quoc Nguyen, Andrea Vázquez Martínez

Bibliographic record

VenueThe Senior Care Pharmacist · 2020
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsPharmacistAmbulatoryMedicineAmbulatory carePsychological interventionPopulationPharmaceutical careOutpatient clinicHealth careClinical pharmacyGeriatricsConfidence intervalFamily medicinePharmacyEmergency medicinePediatricsNursingInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: To compare the impact of a pharmacist's presence for the detection of drug-related problems (DRP) in an interdisciplinary geriatric-ambulatory clinic with a control group without a pharmacist.<br/> DESIGN: Retrospective quasi-experimental study.<br/> SETTING: A geriatric ambulatory-care clinic of a 772-bed tertiary-care teaching hospital in Montreal, Canada.<br/> PARTICIPANTS: A total of 227 ambulatory patients 65 years of age and older presenting to their appointment at the geriatric ambulatory clinic between May 1, 2018, and April 30, 2019.<br/> MAIN OUTCOME MEASURE(S): DRP detected by the interdisciplinary team during the patient evaluation process. Data were collected from clinical notes written by the health care professionals in the electronic medical chart.<br/> RESULTS: The mean age was 80.8 years, and 60.8% of the population were female. Patients were prescribed a mean of 11.3 medications at home. Overall, 636 DRP were detected in the study population. In the adjusted analysis, the difference between the two groups was 2.7 (95% confidence interval 2.0-3.3) DRP detected favoring the group with a pharmacist.<br/> CONCLUSION: The inclusion of a pharmacist in an interdisciplinary team in an ambulatory geriatric-care clinic was associated to a positive impact on care by substantially increasing the number of DRP detected in older patients.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score0.930

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.278
GPT teacher head0.523
Teacher spread0.245 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2020
Admission routes1
Has abstractyes

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