Adopting STOPP/START Criteria Version 3 in Clinical Practice: A Q&A Guide for Healthcare Professionals
Bibliographic record
Abstract
The growing complexity of geriatric pharmacotherapy necessitates effective tools for mitigating the risks associated with polypharmacy. The Screening Tool of Older Persons' Potentially Inappropriate Prescriptions (STOPP)/Screening Tool to Alert doctors to Right Treatment (START) criteria have been instrumental in optimizing medication management among older adults. Despite their large adoption for improving the reduction of potentially inappropriate medications (PIM) and patient outcomes, the implementation of STOPP/START criteria faces notable challenges. The extensive number of criteria in the latest version and time constraints in primary care pose practical difficulties, particularly in settings with a high number of older patients. This paper critically evaluates the challenges and evolving implications of applying the third version of the STOPP/START criteria across various clinical settings, focusing on the European healthcare context. Utilizing a "Questions & Answers" format, it examines the criteria's implementation and discusses relevant suitability and potential adaptations to address the diverse needs of different clinical environments. By emphasizing these aspects, this paper aims to contribute to the ongoing discourse on enhancing medication safety and efficacy in the geriatric population, and to promote more person-centred care in an aging society.
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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 | 0.053 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.025 |
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".