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Record W4405485497 · doi:10.1007/s40266-024-01164-3

Cross-Cultural Adaptation and Clinical Validation of TIME Criteria to Detect Potentially Inappropriate Medication Use in Older Adults: Methodological Report from the TIME International Study Group

2024· article· en· W4405485497 on OpenAlexaff
Tuğba Erdoğan, Büşra Can, Serdar Özkök, Birkan İlhan, Aslı Tufan, Mehmet Akif Karan, A. Benetos, Antonio Cherubini, Michael Drey, Doron Garfinkel, Jerzy Gąsowski, Anna Renom‐Guiteras, Marina Kotsani, Lisa McCarthy, Graziano Onder, Farhad Pazan, Karolina Piotrowicz, Paula A. Rochon, Georg Ruppe, Wade Thompson, Eva Topinková, Nathalie van der Velde, Mirko Petrović

Bibliographic record

VenueDrugs & Aging · 2024
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsUniversity of British ColumbiaWomen's College HospitalUniversity of Toronto
FundersEuropean Commission
KeywordsMedicineTurkishAdaptation (eye)Set (abstract data type)MEDLINEHealth careMedical educationFamily medicineComputer sciencePsychology

Abstract

fetched live from OpenAlex

BACKGROUND: Various explicit screening tools, developed mostly in central Europe and the USA, assist clinicians in optimizing medication use for older adults. The Turkish Inappropriate Medication use in oldEr adults (TIME) criteria set, primarily based on the STOPP/START criteria set, is a current explicit tool originally developed for Eastern Europe and subsequently validated for broader use in Central European settings. Reviewed every three months to align with the latest scientific literature, it is one of the most up-to-date tools available. The tool is accessible via a free mobile app and website platforms, ensuring convenience for clinicians and timely integration of updates as needed. Healthcare providers often prefer to use their native language in medical practice, highlighting the need for prescribing tools to be translated and adapted into multiple languages to promote optimal medication practices. OBJECTIVE: To describe the protocol for cross-cultural and language validation of the TIME criteria in various commonly used languages and to outline its protocol for clinical validation across different healthcare settings. METHODS: The TIME International Study Group comprised 24 geriatric pharmacotherapy experts from 12 countries. In selecting the framework for the study, we reviewed the steps and outcomes from previous research on cross-cultural adaptations and clinical validations of explicit tools. Assessment tools were selected based on both their validity in accurately addressing the relevant issues and their feasibility for practical implementation. The drafted methodology paper was circulated among the study group members for feedback and revisions leading to a final consensus. RESULTS: The research methodology consists of two phases. Cross-cultural adaptation/language validation phase follows the 8-step approach recommended by World Health Organization. This phase allows regions or countries to make modifications to existing criteria or introduce new adjustments based on local prescribing practices and available medications, as long as these adjustments are supported by current scientific evidence. The second phase involves the clinical validation, where participants will be randomized into two groups. The control group will receive standard care, while the intervention group will have their treatment evaluated by clinicians who will review the TIME criteria and consider its recommendations. A variety of patient outcomes (i.e., number of hospital admissions, quality of life, number of regular medications [including over the counter medications], geriatric syndromes and mortality) in different healthcare settings will be investigated. CONCLUSION: The outputs of this methodological report are expected to promote broader adoption of the TIME criteria. Studies building on this work are anticipated to enhance the identification and management of inappropriate medication use and contribute to improved patient outcomes.

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)0.003
metaresearch head score (Gemma)0.003
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 score0.295
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.197
GPT teacher head0.496
Teacher spread0.299 · 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

Citations5
Published2024
Admission routes1
Has abstractyes

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