Development of a core outcome set for clinical trials targeting interventions aiming to improve adherence to appropriate polypharmacy in older people—an international consensus study
Bibliographic record
Abstract
BACKGROUND: Medication non-adherence is prevalent in older people taking polypharmacy. Several interventions have been employed to improve adherence in this population. However, inconsistencies in outcomes have impeded comparisons of findings. Accordingly, this work aimed to develop a core outcome set (COS) for use in trials aiming to improve adherence to appropriate polypharmacy in older people. METHODS: A group of stakeholders, including academics, journal editors, healthcare professionals (HCPs) and public participants, evaluated 13 outcomes compiled from the literature in a Delphi study using a nine-point Likert scale ranging from 1 to 9, where higher scores (7-9) indicated critical importance and lower scores (1-3) unimportance. The resultant Delphi consensus list was discussed and voted on (yes: critical and no: unimportant) in two online nominal group technique (NGT) meetings. The NGT followed a five-stage approach: introduction, silent generation, round-robin, clarification and voting. An outcome was included if ≥80% of participants scored it critical and ≤ 15% scored it as unimportant. RESULTS: Of the 13 outcomes originally presented to participants, consensus was achieved to include six outcomes in the COS after the Delphi study (Round 1, n = 57; Round 2, n = 53; Round 3, n = 50, where 'n' represents participant numbers) and the NGT meetings (n = 10) comprising medication adherence across multiple medications, treatment burden, health-related quality of life (HRQoL), healthcare utilisation (HCU), adverse events and side effects (AEs and SEs) and cost-effectiveness. CONCLUSION: This COS should be used in intervention studies focusing on improving adherence to appropriate polypharmacy in older people. Future work should identify outcome measurement instruments to be used alongside the COS.
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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 | 0.523 | 0.472 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".