Translation and cultural adaptation of MedStopper®—A web-based decision aid for deprescribing in older adults: A protocol
Bibliographic record
Abstract
BACKGROUND: Older patients are more likely to have medication-related problems, which are associated with changes in pharmacokinetics and pharmacodynamics, multimorbidity, and polypharmacy. Polypharmacy and inappropriate prescribing are well-known risk factors which commonly cause adverse clinical outcomes in older people. Prescribers struggle to identify potentially inappropriate medications and to choose an adequate tapering approach. METHODS/DESIGN: The goal of the study is to translate and culturally adapt MedStopper®, an original English language web-based decision aid system in deprescribing medication, to the Portuguese population. A translation-back translation method, with validation of the obtained Portuguese version of MedStopper® will be used, followed by a comprehension test. DISCUSSION: This is the first research in the Portuguese primary care setting that aims to provide a useful online tool for the appropriate prescription of older patients. The translated version in Portuguese version of the MedStopper® tool will represent an advance that seeks to continue improving the management of medications in the elderly. The adaptation into Portuguese of the educational tool provides clinicians with a screening tool to detect potentially inappropriate prescribing in patients older than 65 that reliable and easier to use. TRIAL REGISTRATION: Retrospectively registered.
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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 | 0.025 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.049 | 0.010 |
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".