Magnesium for the Management of Chronic Noncancer Pain in Adults: Protocol for a Systematic Review (Preprint)
Bibliographic record
Abstract
BACKGROUND Chronic pain is a highly prevalent and complex health problem that is associated with a severe symptom burden, as well as substantial economic and social impact. Many patients with chronic pain still suffer from unrelieved or undertreated pain due to the incomplete efficacy and dose-limiting adverse effects of current therapies. Long-term and high-dose opioid use has considerably increased in the past 20 years despite limited evidence supporting its effectiveness in several chronic pain conditions, and serious concerns have emerged regarding adverse effects and potential misuse. Until recently, the steady increase in opioid prescribing rates has been associated with rising opioid-related mortality and other serious problems, emphasizing the need for better nonopioid therapies. Emerging evidence supports the safe use of magnesium in controlling chronic pain, but its overall efficacy and safety is still unclear. OBJECTIVE This paper aims to assess the efficacy and safety of magnesium compared with a placebo for the treatment of chronic noncancer pain. METHODS We will conduct a detailed search on Cochrane Central Register of Controlled Trials, MEDLINE, and EMBASE from their inception until the date the searches are run to identify relevant randomized controlled trials. The reference lists of retrieved studies as well as Web-based trial registries will also be searched. We will include randomized double-blind trials comparing magnesium (at any dose, frequency, or route of administration) with placebo using participant-reported pain assessment. Two reviewers will independently evaluate studies for eligibility, extract data, and assess trial quality and potential bias. Risk of bias will be assessed using criteria outlined in the Cochrane Handbook for Systematic Review of Interventions. Primary outcomes for this review will include any validated measure of pain intensity or pain relief. Dichotomous data will be used to calculate the risk ratio and number needed to treat or harm. The quality of evidence will be assessed using the Grading of Recommendations Assessment, Development and Evaluation approach. RESULTS This protocol is grant-funded and has undergone a peer-review process through the Queen’s University Department of Anesthesiology and Perioperative Medicine Vandewater Endowed Studentship. This project is also supported, in part, by the Chronic Pain Network of the Canadian Institutes of Health Research Strategy for Patient-Oriented Research. The electronic database search strategies are currently being developed and modified. The entire review is expected to be completed by January 1, 2019. CONCLUSIONS The completion of this review is expected to identify available high-quality evidence describing the efficacy and safety of magnesium for the treatment of chronic noncancer pain. INTERNATIONAL REGISTERED REPOR PRR1-10.2196/11654
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.032 | 0.050 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.013 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.080 | 0.008 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".