Systematic Analysis of Publication Bias in Neurosurgery Meta-Analyses
Bibliographic record
Abstract
BACKGROUND: Statistically significant positive results are more likely to be published than negative or insignificant outcomes. This phenomenon, also termed publication bias, can skew the interpretation of meta-analyses. The widespread presence of publication bias in the biomedical literature has led to the development of various statistical approaches, such as the visual inspection of funnel plots, Begg test, and Egger test, to assess and account for it. OBJECTIVE: To determine how well publication bias is assessed for in meta-analyses of the neurosurgical literature. METHODS: A systematic search for meta-analyses from the top neurosurgery journals was conducted. Data relevant to the presence, assessment, and adjustments for publication bias were extracted. RESULTS: The search yielded 190 articles. Most of the articles (n = 108, 56.8%) were assessed for publication bias, of which 40 (37.0%) found evidence for publication bias whereas 61 (56.5%) did not. In the former case, only 11 (27.5%) made corrections for the bias using the trim-and-fill method, whereas 29 (72.5%) made no correction. Thus, 111 meta-analyses (58.4%) either did not assess for publication bias or, if assessed to be present, did not adjust for it. CONCLUSION: Taken together, these results indicate that publication bias remains largely unaccounted for in neurosurgical meta-analyses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.278 | 0.353 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.080 | 0.070 |
| Bibliometrics | 0.022 | 0.056 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 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; both teacher heads 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".