MétaCan
Menu
Back to cohort
Record W4220800238 · doi:10.1093/ijpp/riac019.034

Language used to describe medication review activities: does it require standardisation? A narrative synthesis

2022· article· en· W4220800238 on OpenAlexaboutno aff
M. R. Alharthi, Jeanette Blacklock, Sion Scott, D. M. Wright

Bibliographic record

VenueInternational Journal of Pharmacy Practice · 2022
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePsychological interventionData extractionSystematic reviewMEDLINEPopulationGrey literatureConsolidated Standards of Reporting TrialsIntervention (counseling)Critical appraisalDescriptive statisticsHealth careAlternative medicineFamily medicineNursingPathology

Abstract

fetched live from OpenAlex

Abstract Introduction Medication review (MR) is a health care professional’s systematic assessment of a patient’s medications with recommendations for improvement (1). To enable comparison between different evaluations of medication review-based interventions to determine whether the nature of activity differs, it is important that standardised language is used. Currently, there is no accepted international taxonomy for describing medication review activities. Therefore, we undertook a systematic review of literature with narrative synthesis to determine whether standardisation may be appropriate. Aim To determine the range of terms used to describe medication review activities. Method A PROSPERO registered systematic review (CRD 42020215992) was performed using search terms according to the Population, Intervention, Comparison and Outcome (PICO) framework. ‘Population’ & ‘Comparison’ were not used. Synonyms for medication review activities were used for both ‘Intervention’ and ‘Outcomes’, e.g., stop, start, change, alter. We included all papers reporting medication review activities in English with empirical data. Papers not using language to describe medication review activities were excluded. Two researchers reviewed all titles, abstracts, and full-text papers independently; discussion resolved any disagreement. Data extraction was carried out independently as per Cochrane Effective Practice and Organisation of Care (EPOC) as follows: The papers were assessed using the Mixed Method Appraisal Tool (MMAT). The research team themed the extracted terms. Results After deleting duplicates, 9746 titles were screened. Twenty-one studies were included: eight quantitative non-randomised trials, eight randomised controlled trials, and five quantitative descriptive studies. The studies covered the UK, Netherlands, Australia, Sweden, Norway, Belgium, Canada, and Jordan. The table summarises the medication review activities reported in these papers. Conclusion Various authors reported medication review activities. ‘Alter’ and ‘adaptation’ are examples of ambiguous terms. Determining whether actions are related with activities to reduce or increase doses is difficult due to such terminology. As a result, comparing medication review approaches may be difficult. Limiting the search strategy to English-language only may have missed some studies. A taxonomy to describe and define medication review activities, thereby standardising MR reporting, should improve the presentation of data from process evaluations and the ability to compare activity between studies. Reference (1) Christensen M, Lundh A. Medication review in hospitalised patients to reduce morbidity and mortality. Cochrane Database Syst Rev [Internet]. 2016 Feb 20; Available from: https://doi.wiley.com/10.1002/14651858.CD008986.pub3

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.406
metaresearch head score (Gemma)0.684
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.406
Threshold uncertainty score0.733

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4060.684
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0130.009
Bibliometrics0.0390.041
Science and technology studies0.0030.009
Scholarly communication0.0160.019
Open science0.0060.012
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0160.003

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 head0.123
GPT teacher head0.498
Teacher spread0.375 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Pharmacy PracticeSame topicPharmaceutical Practices and Patient OutcomesFrench-language works237,207