Systematic Reviews: A Primer for Plastic Surgery Research
Bibliographic record
Abstract
Clinicians rely on review articles to keep current with the rapid accumulation of medical and surgical literature. Traditional expert reviews, however, often suffer from inherent personal biases and may not reflect a true synthesis of the existing literature on a particular subject. Systematic reviews are structured, scientific articles that address the shortcomings of traditional reviews by adhering to strict, reproducible methods and recommended guidelines. The methods are designed to eliminate possible sources of bias, ensure as complete a review of the existing literature as possible, and present the results in a way that is useful for its intended audience. Systematic reviews may at times include a quantitative synthesis of the available data in the form of a meta-analysis. Meta-analysis is a statistical tool for combining the numerical results of separate studies to obtain a summary outcome with increased precision due to the larger, combined number of patients. Meta-analyses may be particularly helpful when individual study results are conflicting and the existing literature is inconclusive. The validity of meta-analysis, however, is highly dependent on the quality of data available in the literature. In its strictest form, meta-analysis is used to combine data from only randomized controlled clinical trials. Because randomized controlled clinical trials are infrequently performed in plastic surgery research, this article will focus on systematic reviews to provide the readers with a useful guide in performing this field of study.
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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 | 0.174 | 0.296 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.007 |
| Bibliometrics | 0.037 | 0.031 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.015 | 0.027 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".