Systematic Review of Outcome Measures in Pharmacologically Managed Chronic Pain: Informing a New Outcome Framework for Healthcare Provider‐Led Pharmacotherapy Services
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Chronic pain represents a global burden, highlighting the necessity for accurate outcome measures in treatment evaluation. This systematic review aims to identify what outcome measures and tools are applied in chronic pain primary care-based pharmacotherapy services. DATABASES AND DATA TREATMENT: The MEDLINE, Embase, and CINAHL databases, along with the reference lists of published articles, were systematically searched from 2013 to July 2023. This search included observational studies that employed pharmacological interventions recommended by the World Health Organisation pain ladder and the Scottish Intercollegiate Guidelines Network guidelines. The studies targeted chronic pain patients treated in outpatient settings and examined five predefined outcomes: health-related quality of life (HRQoL), cost-effectiveness, medication optimisation, adverse events, and patient experience. The quality of included studies was assessed using the Newcastle-Ottawa Scale (NOS). RESULTS: Among the 23 studies included a total of 51 outcome measurement tools were employed to assess the five predefined outcomes, involving 44,472 patients with chronic pain. Fifteen were cohort studies, while 8 were cross-sectional surveys or questionnaire-based. Most studies focused on one to two outcomes only (n = 19; 82.6%). HRQoL emerged as the primary outcome studied across all 23 studies (100%), predominantly assessed through the Brief Pain Inventory (BPI) tool (n = 9; 39.1%). Conversely, the least studied outcomes were medication optimisation and cost-effectiveness. The timing of measurement post-intervention and follow-up durations displayed significant variability across the studies. CONCLUSIONS: This review identifies gaps in enabling a more holistic assessment of pharmacotherapy services and underscores the need for enhanced consistency via standardised tools in clinical practice.
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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 | 0.065 | 0.189 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.012 |
| Bibliometrics | 0.024 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".