Methodological Issues in Systematic Reviews and Meta-Analyses of Observational Studies in Orthopaedic Research
Bibliographic record
Abstract
The validity and applicability of a systematic review depends on the quality of the primary studies that are included and the quality of the methods used to conduct the review itself. Sometimes, observational studies represent the best available evidence. Subject to selection, information, and confounding biases, observational studies are thought to overestimate treatment or exposure effects. A systematic review of observational data must therefore attempt to minimize or prevent these sources of bias by developing explicit but also broad inclusion and exclusion criteria focused on extracting the best available evidence relevant to the review question. Systematic reviews must also make use of an expansive search strategy, with use of multiple resources, to demonstrate the reproducibility of selection and quality-assessment criteria, to perform a quantitative analysis and adjustment for confounding where appropriate, and to explore possible reasons for differences between the results of the primary studies. In this paper, we address the advantages and limitations of systematic reviews and meta-analyses of observational studies and suggest solutions at the design phase of protocol development.
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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 | 0.774 | 0.907 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.017 | 0.020 |
| Bibliometrics | 0.026 | 0.040 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.013 | 0.011 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".