Multimorbidity and analgesic-related harms: a systematic review
Bibliographic record
Abstract
BACKGROUND: Multimorbidity is the presence of two or more long-term medical conditions. Chronic pain affects more than half of people with multimorbidity, and optimal treatment strategies are unknown. We aimed to quantify the risk of adverse outcomes from the following analgesics: opioids, nonsteroidal anti-inflammatory drugs (NSAIDs), and gabapentinoids in adults with multimorbidity. METHOD: The review was registered on PROSPERO (CRD42023462592). We searched Medline, CINAHL, Web of Science, Embase, and CENTRAL for studies reporting analgesic-related harms in people with multimorbidity or the impact of multimorbidity on harms in adults exposed to analgesics. Two researchers independently screened titles/abstracts, completed full-text reviews, extracted data, and assessed risk of bias using the Newcastle-Ottawa scale. Studies were synthesised narratively, grouping by analgesic class and direction of effect. RESULTS: We screened 6690 records and 344 full texts, with 27 studies included (n=2 671 958 patients). Studies were heterogenous, with variable quality (high risk of bias, n=11). Most studies on opioids reported adverse outcomes (12/16). Opioid use compared with non-use was associated with increased mortality in adults with multimorbidity. Multimorbidity was associated with opioid overdose and death among adults prescribed opioids for pain. Half of studies of NSAIDs reported adverse outcomes (6/11) including gastrointestinal bleeding. Only one study assessed gabapentinoids which found an association with delirium and pneumonia, but not mortality in people with multimorbidity. CONCLUSIONS: There is evidence of harms associated with opioids in adults with multimorbidity, including overdose and increased mortality. There is a lack of evidence on gabapentinoids. Further research is required to understand optimal analgesic management in people with multimorbidity. SYSTEMATIC REVIEW PROTOCOL: PROSPERO (CRD42023462592).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".