Impact of eliciting treatment priorities on analgesic prescribing in older patients with high levels of polypharmacy
Bibliographic record
Abstract
BACKGROUND: Multimorbidity guidelines recommend tailoring care to patients' priorities. The Supporting Prescribing in Multimorbidity in Primary Care (SPPiRE) trial focused on optimizing medicines use in older adults with significant polypharmacy and tailoring prescribing and deprescribing to individual priorities. This study aimed to compare self-reported and general practitioner (GP)-recorded patient priorities and examine the impact of prioritizing pain on analgesic prescribing. METHODS: This secondary cohort analysis of the SPPIRE trial and process evaluation assessed baseline participant-identified priorities and intervention group GP-recorded priorities during medication reviews with agreement assessed using Cohen's kappa. Analgesic prescribing patterns and daily morphine milligram equivalents changes during the study period were summarized. The impact of pain (self-reported, GP-recorded, and severe or extreme pain on the baseline EQ5D) on opioid intensification was analysed using multi-level models accounting for GP practice clustering and intervention effects. RESULTS: A total of 403 patients (mean age 76.5 years) were included; 178 (44.2%) reported pain as a priority at baseline. Agreement between self-reported and GP-recorded pain was poor (kappa 0.118, P = 0.05). Most analgesic prescriptions decreased during the study, except for potent opioids, which increased in both trial arms. All three pain variables were associated with increased odds of opioid intensification at follow-up. CONCLUSION: In this older population of patients with significant polypharmacy, identifying pain as a priority was associated with an increased likelihood of opioid intensification, despite guidelines advising against their use for chronic pain. This study highlights the challenges faced by GPs treating pain in older adults with multimorbidity.
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".