Understanding Systematic Reviews and Meta-analyses in Orthopaedics
Bibliographic record
Abstract
The systematic literature review is a powerful tool for summarizing and evaluating current knowledge related to a specific research question. Systematic reviews have many advantages over traditional narrative reviews. A meta-analysis of data from a systematic review can provide a better estimate of a treatment effect than can individual studies. To ensure quality conclusions, rigorous methods must be applied to systematic reviews, such as formulation of a specific research question, systematic literature search, selection and assessment of included studies, data extraction, quality assessment of included studies, meta-analysis and presentation of results, and formation of conclusions. Threats to the internal validity and generalizability of the conclusions of systematic reviews include lack of clarity or appropriateness of the research question, poor quality of the included studies, heterogeneity of results between studies, inappropriate conclusions, and inappropriate application in clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.180 | 0.443 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.011 |
| Bibliometrics | 0.027 | 0.021 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".