Evaluating the efficacy of oxytocin for pain management: an updated systematic review and meta-analysis
Bibliographic record
Abstract
Currently available treatments for chronic pain rarely result in full recovery, indicating the need for an analgesic that is non-addictive and effective. Oxytocin has recently gained attention for its potential analgesic properties. We searched Ovid MEDLINE®, Embase, PsycINFO and CINAHL (from January 2012 to February 2022) and the Clinicaltrials.gov website. Studies from 1950- 2012 were included from our published review (Rash et al., 2014). Comprehensive Meta-Analysis software was used where three or more studies reported on the same outcome. Narrative synthesis was performed for outcomes with less than three studies by calculating individual effect sizes. Searches returned 2,087 unique citations, 8 of which met inclusion criteria. 6 studies were included from Rash et al. (2014; N= 1,504). Three metaanalyses were conducted to evaluate the effect of exogenous oxytocin on pain, the association between endogenous oxytocin and self-reported pain ratings and the effect of exogenous oxytocin on self-reported depression. The effect of exogenous oxytocin on acute pain and emotional function, and the association between endogenous oxytocin and self-reported anxiety were narratively reviewed. There was a trend favouring oxytocin as an analgesic despite nonsignificant meta-analysis. Results from meta-analysis and narrative review were mixed and highlighted potential sex differences but heterogeneity in the included studies precludes definitive conclusions from being drawn. Future studies are imperative and should undertake more precise exploration of mechanisms of analgesic action to clarify inconsistency in the existing body of literature.
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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 | 0.026 | 0.060 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.035 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 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".