User's Guide to a Meta-Analysis about an Orthopaedic Implant
Bibliographic record
Abstract
Meta-analyses can be an excellent method to summarize the existing literature of studies concerning orthopaedic implants and devices. It is important to understand how meta-analyses are conducted and to be able to evaluate whether a meta-analysis has strong methodological rigor to help with clinical decisions. This paper begins with an overview of what a meta-analysis is and why it is useful. The second section provides the important characteristics of conducting a meta-analysis. The third section will provide detail of how to interpret a meta-analysis, including topics such as the quality of the included studies, comparing the results between studies, pooling data, and how to interpret the results. The benefits and limitations are presented, along with recommendations of how to ensure future high-quality meta-analyses. Meta-analyses are useful for synthesizing the results of multiple primary studies and can provide excellent evidence for clinical decisions; however, it is important that methodological flaws are limited.
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 | 0.015 | 0.131 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.352 | 0.096 |
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".