Examining Heterogeneity in Meta-Analysis
Bibliographic record
Abstract
STUDY DESIGN: Literature review. OBJECTIVE: To assess the influence of various factors in statistical heterogeneity of meta-analyses of interventions for low back pain. One of these factors was study design: randomized controlled trial (RCT) versus nonrandomized study (NRS). SUMMARY OF BACKGROUND DATA: The presence of statistical heterogeneity poses a challenge to the conduct and interpretation of meta-analyses. METHODS: We searched MEDLINE, EMBASE, and The Cochrane Library up to May 2005 for comparative studies of interventions for low back pain. The interventions with the highest number of NRSs were selected. All NRSs and RCTs of the same interventions were combined using meta-analysis. Subgroup analyses and meta-regression were performed according to study design and other factors that were selected by a panel of 20 experts. RESULTS: NRSs frequently either agree with RCTs or underestimate the effects compared with RCTs. The interventions and the respective factors that explained statistical heterogeneity were a) surgery versus conservative treatments (17 NRSs and 8 RCTs): study design (odds ratio, OR: 1.56 and 4.69 for nonrandomized and randomized studies, respectively), pain duration (OR: 1.75 and 3.55 for chronic and acute, respectively), and involvement of workers' compensation (OR: 1.85 and 5.07, with and without, respectively); b) surgery with fusion versus surgery without fusion (17 NRSs and 3 RCTs): spondylolisthesis (OR: 2.15 and 1.22, with and without, respectively); c) Instrumented fusion versus noninstrumented fusion (15 NRSs and 8 RCTs): previous surgery (OR: 2.89 and 1.36, with and without, respectively) and levels fused (OR: 1.50 and 2.98, single and multilevel, respectively). CONCLUSION: Comparisons between RCTs and NRSs may be influenced by various factors, including study design. However, other factors were more powerful explanatory variables than study design. These factors included pain duration, involvement of workers' compensation, presence of spondylolisthesis, previous surgery, and levels fused.
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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 | 0.138 | 0.338 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.029 | 0.055 |
| Bibliometrics | 0.024 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".