The GRADE Working Group and CINeMA approaches provided inconsistent certainty of evidence ratings for a network meta-analysis of opioids for chronic noncancer pain
Bibliographic record
Abstract
OBJECTIVES: Assessment of the certainty of evidence (CoE) from network meta-analysis is critical to convey the strength of inferences for clinical decision-making. Both the GRADE (Grading of Recommendations Assessment, Development and Evaluation) Working Group (GWG) and the Confidence in Network Meta-Analysis (CINeMA) framework have been designed to assess the CoE of treatment effects informed by network meta-analysis; however, the concordance of results is uncertain. STUDY DESIGN AND SETTING: We assessed the CoE for treatment effects of individual opioids on pain relief and physical functioning from a network meta-analysis for chronic noncancer pain using the GWG approach and the CINeMA framework. Both approaches evaluate the CoE as high, moderate, low or very low. We quantified the number of discrepant CoE ratings between approaches and the magnitude of the difference (ie, one level, two levels, or three levels). RESULTS: Across 105 comparisons among individual opioids for pain relief, the GWG and CINeMA approaches provided different CoE ratings in 34% of cases (36 of 105). Across 66 comparisons for physical functioning, there was discordance in 17% of cases (11 of 66). All discrepancies were separated by one level. The CINeMA framework typically provided lower CoE ratings compared to the GWG approach, predominantly because of differences in the assessment of transitivity and heterogeneity. CONCLUSION: Our findings suggest there are differences between the CoE ratings provided by the GWG and CINeMA approaches when applied to network meta-analyses. Further research is needed to replicate or refute our findings in other network meta-analyses and assess the implications for clinical decision-making.
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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 | 0.518 | 0.837 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.035 |
| Bibliometrics | 0.022 | 0.016 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".