Lessons Learned From Our Elders: How to Study Polypharmacy in Populations With Intellectual and Developmental Disabilities
Bibliographic record
Abstract
Polypharmacy is the concurrent use of multiple medications, including both psychotropic and non-psychotropic drugs. Although it may sometimes be clinically indicated, polypharmacy can have a number of negative consequences, including medication nonadherence, adverse drug reactions, and undesirable drug-drug interactions. The objective of this paper was to gain a better understanding of how to study polypharmacy among people with intellectual and developmental disabilities (IDD). To do this, we reviewed literature on polypharmacy among the elderly and people with IDD to inform future research approaches and methods on polypharmacy in people with IDD. Results identified significant variability in methods used to study polypharmacy, including definitions of polypharmacy, samples studied, analytic strategies, and variables included in the analyses. Four valuable methodological lessons to strengthen future polypharmacy research in individuals with IDD emerged. These included the use of consistent definitions of polypharmacy, the implementation of population-based sampling strategies, the development of clinical guidelines, and the importance of studying associated variables.
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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 | 0.039 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.013 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".