A protocol for a systematic review for perioperative pregabalin use
Bibliographic record
Abstract
BACKGROUND: Perioperative pain management has recently been revolutionized with the recognition of novel mechanisms and introduction of newer drugs. Many randomized trials have studied the use of the gabapentinoid anti-epileptic, pregabalin, in acute pain. Published systematic reviews suggest that using pregabalin for perioperative pain management may decrease analgesic requirements and pain scores, at the expense of troublesome side effects. A major limitation of the extant reviews is the lack of rigorous investigation of clinical characteristics that would maximize the benefit harms ratio in favor of surgical patients. We posit that effects of pregabalin for perioperative pain management vary by the type of surgical pain model and propose this systematic review protocol to update previous systematic reviews and investigate the heterogeneity in findings across subgroups of surgical pain models. METHODS/DESIGN: Using a peer-reviewed search strategy, we will search key databases for clinical trials on perioperative pregabalin use in adults. The electronic searches will be supplemented by scanning the reference lists of included studies. No limits of language, country or year will be imposed. Outcomes will include pain; use of co-analgesia, particularly opioids; enhanced recovery; and drug-related harms. We will focus on the identification of surgical models and patient characteristics that have shown benefit and adverse effects from pregabalin.Two clinical experts will independently screen the studies for inclusion using eligibility criteria established a priori. Data extracted by the reviewers will then be verified. Publication bias will be assessed, as will risk of bias using the Cochrane Risk of Bias tool. Meta-analysis and meta-regression are planned if the studies are deemed statistically, methodologically and clinically homogenous. Evidence will be graded for its strength for a select number of outcomes. DISCUSSION: We will explore the findings of perioperative clinical trials studying the use of pregabalin for acute pain. We will comment on the implications of the findings and provide further direction for the appropriate use of pregabalin in acute pain. This protocol will attempt to bridge the growing gap between clinical experience and emerging evidence, and has the potential to aid future guideline development in the perioperative use of pregabalin. TRIAL REGISTRATION: PROSPERO registration number CRD42012002078.
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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 | 0.104 | 0.158 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.017 | 0.015 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.140 | 0.021 |
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".