Investigating pain-related medication use and contribution to polypharmacy in adults with intellectual disabilities: a systematic review
Bibliographic record
Abstract
BACKGROUND: Adults with intellectual disability experience more pain than adults without and, despite a higher number of medications being prescribed, may be less likely to receive medication for pain. We conducted a systematic review of existing literature on medication for pain and painful conditions in adults with intellectual disability to explore if there is any association with polypharmacy, multimorbidity or demographic characteristics. METHODS: This systematic review followed PRISMA guidelines. Medline, Embase, PubMed, PsycINFO, Web of Science, CINAHL, Cochrane Library and Scopus were searched from January 2000 to 21st October 2024. We included original, peer-reviewed observational, qualitative or mixed-method studies published in English with data on medication for pain or painful conditions in adults with intellectual disability. Two independent reviewers performed study selection, data extraction, and quality assessment; disagreements were resolved by a third reviewer. Adapted Newcastle-Ottawa Scale or the Critical Appraisal Skills Programme for qualitative studies was used for quality assessment of included studies and findings were reported via narrative synthesis. PROSPERO registration: CRD42023415051. RESULTS: Twenty-seven of 26,170 articles met the eligibility criteria. Adults with intellectual disability were more likely to have simple analgesic medication than non-steroidal anti-inflammatory drugs, opioids or adjuvant pain medications than the general population. Psychotropic medications were more commonly prescribed in adults with intellectual disability than medication for pain or painful conditions. Adults with intellectual disability and caregivers reported under-recognition and most likely under-treatment of pain. CONCLUSIONS: Adults with intellectual disability may receive less pharmacological management of pain with analgesics and medication for painful conditions despite the high prevalence of polypharmacy, suggesting pain is under-treated. Better assessment and pharmacological treatment of pain and painful conditions is a key future research priority to address this health inequality and improve quality of life for this vulnerable group of people.
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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 | 0.010 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".