Guidance synthesis. Medical research for and with older people in Europe: Proposed ethical guidance for good clinical practice: Ethical considerations
Bibliographic record
Abstract
INTRODUCTION: In Europe the population is ageing rapidly. Older people are taking many medicinal products daily and these may not necessarily be suitable for them. Publications show that older patients are underrepresented in clinical trials, especially those over 75 years, with multiple co-morbidities, concomitant treatments and/or frailty. This document provides a summary of recommendations on ethical aspects of clinical trials with older people, who may in some cases be considered a vulnerable patient population. The EFGCP's Geriatric Medicine Working Party (GMWP) has developed this guidance to promote such research and to support health care professionals in their efforts. ETHICAL, SCOPE AND CONTEXT: The definition of a geriatric patient is reviewed. Frail and vulnerable patients, who are a minority of geriatric patients, should be included whenever it is relevant. The legal context is described. THE PROCESS OF INFORMED CONSENT: All adults should be presumed capable of consent, unless proven otherwise; informed consent must be sought for all older people who are able to consent. A simple, short and easy-to-understand information sheet and consent form will contribute to improving the readability and understanding of the older participant. A participant guide and the use of a simple tool to ensure decision making capacity, are recommended. Whenever older people are unable to consent, their assent should be sought systematically using adequate information, in addition to seeking the consent of their legal or authorised representative as appropriate. ETHICS COMMITTEES: Research ethics committees need internal and/or external geriatric expertise to balance the benefits and risks of research in older people and to appreciate and recognise their autonomy. DESIGN AND ANALYSES: Design and Analyses should be adapted to the objectives with appropriate outcomes and are not different from other clinical trials. CONCLUSIONS: The absence of proper recruitment or insufficient presence of older patients in clinical development plans for new medicinal products is detrimental; there is a need to improve evidence-based knowledge, understanding and management of their conditions and treatment. The aim of this guidance is to facilitate clinical research for and with the older patient population. The long version of the guidance will be available on the EFGCP's website: www.efgcp.be/.
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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 | 0.160 | 0.380 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.013 | 0.012 |
| Research integrity | 0.074 | 0.030 |
| Insufficient payload (model declined to judge) | 0.039 | 0.039 |
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".