A step-by-step guide for systematic reviews and meta-analyses in spine surgery-study execution: a narrative review
Bibliographic record
Abstract
Background and Objective: Systematic reviews and meta-analyses are crucial in spine surgery, offering a robust approach to integrating evidence from multiple studies and guiding clinical decision-making. These reviews resolve inconsistencies across studies and increase statistical power, making them indispensable for assessing the effectiveness and safety of surgical interventions. This review serves as a comprehensive guide on how to: (I) design; (II) implement; and (III) publish a systematic review in spine surgery. Methods: We conducted a narrative review by searching key databases, including PubMed, Embase, Cochrane Library, Scopus, and Web of Science, to identify studies that demonstrate the best practices in conducting systematic reviews in spine surgery. Studies were selected for their methodological rigor and adherence to Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The included systematic reviews were evaluated based on how they applied standardized quality assessment tools, such as the Cochrane risk of bias (RoB) tool for randomized controlled trials (RCTs) and the Methodological Index for Non-Randomized Studies (MINORS) criteria or Newcastle-Ottawa Scale (NOS) for non-randomized studies. A narrative synthesis was performed to summarize the findings and highlight best practices. Key Content and Findings: Systematic reviews stand at the pinnacle of the evidence-based hierarchy in spine surgery, integrating findings from various primary investigations. This study explores techniques for evaluating data quality using tools such as the Cochrane RoB, the MINORS criteria, and the NOS. We detail methods for interpreting and analyzing data, and we outline the process of transforming the findings into a publishable manuscript, with reference to a previously published example. Adhering to the PRISMA guidelines is advocated as a standard across all scientific literature, inclusive of spine surgery. Presenting data through pooled analyses with Forest Plots, along with odds ratios and 95% confidence intervals, is a customary practice. Conclusions: In the manuscript preparation phase, it is vital to address and debate the intrinsic limitations of systematic reviews, such as their selection criteria and the overall quality, which may be constrained by the caliber of the included studies (e.g., publication bias, heterogeneity, search/selection bias).
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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 | 0.278 | 0.462 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.011 | 0.015 |
| Bibliometrics | 0.031 | 0.023 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.011 | 0.012 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.023 | 0.014 |
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".