Pharmacological interventions for chronic pain in children: an overview of systematic reviews
Bibliographic record
Abstract
We know little about the safety or efficacy of pharmacological medicines for children and adolescents with chronic pain, despite their common use. Our aim was to conduct an overview review of systematic reviews of pharmacological interventions that purport to reduce pain in children with chronic noncancer pain (CNCP) or chronic cancer-related pain (CCRP). We searched the Cochrane Database of Systematic Reviews, Medline, EMBASE, and DARE for systematic reviews from inception to March 2018. We conducted reference and citation searches of included reviews. We included children (0-18 years of age) with CNCP or CCRP. We extracted the review characteristics and primary outcomes of ≥30% participant-reported pain relief and patient global impression of change. We sifted 704 abstracts and included 23 systematic reviews investigating children with CNCP or CCRP. Seven of those 23 reviews included 6 trials that involved children with CNCP. There were no randomised controlled trials in reviews relating to reducing pain in CCRP. We were unable to combine data in a meta-analysis. Overall, the quality of evidence was very low, and we have very little confidence in the effect estimates. The state of evidence of randomized controlled trials in this field is poor; we have no evidence from randomised controlled trials for pharmacological interventions in children with cancer-related pain, yet cannot deny individual children access to potential pain relief. Prospero ID: CRD42018086900.
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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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".