Systematic review and meta-analysis in cardiac surgery
Bibliographic record
Abstract
PURPOSE OF REVIEW: The purpose of this article is to review the strengths and weaknesses of systematic reviews and meta-analyses to inform our current understanding of cardiac surgery. RECENT FINDINGS: A systematic review and meta-analysis of a focused topic can provide a quantitative estimate for the effect of a treatment intervention or exposure. In cardiac surgery, observational studies and small, single-center prospective trials provide most of the clinical outcomes that form the evidence base for patient management and guideline recommendations. As such, meta-analyses can be particularly valuable in synthesizing the literature for a particular focused surgical question. Since the year 2000, there are over 800 meta-analysis-related publications in our field. There are some limitations to this technique, including clinical, methodological and statistical heterogeneity, among other challenges. Despite these caveats, results of meta-analyses have been useful in forming treatment recommendations or in providing guidance in the design of future clinical trials. SUMMARY: There is a growing number of meta-analyses in the field of cardiac surgery. Knowledge translation via meta-analyses will continue to guide and inform cardiac surgical practice and our practice guidelines.
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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 | 0.044 | 0.141 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.033 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".