Selection of outcome measurement instruments for a core outcome set for trials aimed at improving appropriate polypharmacy in older people in primary care: a Delphi consensus study
Bibliographic record
Abstract
Abstract Background Despite developing a polypharmacy core outcome set (COS) in primary care, it is not clear how these outcomes should be measured. Aim To select outcome measurement instruments (OMIs) for a COS targeting appropriate polypharmacy in older patients in primary care. Method Following the Consensus-based Standards for the selection of health Measurement Instruments (COSMIN) guideline, OMIs were identified from a Cochrane review focusing on appropriate polypharmacy. The quality of OMIs was assessed using a published checklist. Subsequently, two rounds of Delphi questionnaires were conducted via the SoGoSurvey® platform, engaging stakeholders (researchers, clinicians and journal editors specialising in geriatric primary care) to achieve consensus on OMIs using a scale encompassing “agree”, “disagree”, or “unsure”. Consensus was achieved if 70% or more participants chose “agree” and 15% or fewer chose “disagree.” Results The quality of 20 OMIs identified from the Cochrane review was evaluated. Seven OMIs were selected based on meeting the COSMIN guideline’s minimum requirements. Out of 188 potential participants, 57 (30.3%) consented to participate. Rounds 1 and 2 of Delphi exercises were completed by 50 respondents, achieving agreement on three OMIs: ‘number of serious adverse drug reactions (ADRs)’ (98%), ‘number of deaths’ (76%), and ‘number of patients who fell’ (70%) for measuring ‘serious ADRs,’ ‘mortality,’ and ‘falls,’ respectively. No agreement was reached for ‘medication appropriateness,’ ‘medication side-effects,’ ‘quality of life,’ and ‘medication regimen complexity.’ Conclusion OMIs were selected for a limited number of outcomes in the polypharmacy COS. Future research should identify suitable OMIs for the remaining four outcomes.
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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 | 0.543 | 0.516 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".