Author guidelines for conducting systematic reviews and meta‐analyses
Bibliographic record
Abstract
This article is a guidance how to write systematic reviews (SR's) and meta-analyses (MA) in orthopaedics and which aspects to focus on for transparency, systematicity and readability. Both SR and MA summarise and synthesise the best evidence available on a specific topic. This requires a systematic, structured and transparent process of analysis. The title should be concise, indicate type of review and ideally report the most important finding. Next, the structured abstract (no more than 350 words) should also raise key points and report the overall level of evidence. A relevant clinical question must be defined before the literature search is started. Methodological details such as databases searched, the exact search strategy (including time frame), inclusion/exclusion criteria, method of literature appraisal and statistical analysis must be described briefly. The primary and secondary outcomes should be mentioned. SR's be pre-registered before data extraction, to ensure transparency and the reduction of risk of bias. If registered, registration number should be stated in the abstract and the funding sources. A clear summary of the findings is important including the number of identified studies (depicted in a flowchart) and for meta-analyses a forest plot. The results of the literature appraisal and statistical analyses should be reported precisely. Subsequently, a general interpretation of findings and their significance and relevance to clinical practice should be provided. Clinical implications from the analysis should be drawn carefully and further research questions should be addressed. Finally, a conclusion, based solely on the results of the study is a necessity. Up to ten keywords are requested representing the main content of the article. Most applicable keywords should facilitate finding the manuscript in the databases and therefor considered carefully.
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 | 0.173 | 0.471 |
| Meta-epidemiology (narrow) | 0.005 | 0.009 |
| Meta-epidemiology (broad) | 0.011 | 0.018 |
| Bibliometrics | 0.022 | 0.029 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.011 | 0.006 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.156 | 0.087 |
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".