Methodologic Issues in Systematic Reviews and Meta-Analyses
Bibliographic record
Abstract
Systematic reviews of original research are increasing in number. Systematic reviews are distinct from narrative reviews because they address a specific clinical question, require a comprehensive literature search, use explicit selection criteria to identify relevant studies, assess the methodologic quality of included studies, explore differences among study results, and either qualitatively or quantitatively synthesize study results. Systematic reviews that quantitatively pool results of more than one study are called meta-analyses. Several organizations are collaboratively involved in producing high quality systematic reviews and meta-analyses. Familiarity with how to do a systematic review and meta-analysis will lead to greater skill in using this type of article. For clinicians, teachers, and investigators, systematic reviews and meta-analyses are useful sources of evidence.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | medium |
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 | 0.749 | 0.902 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.015 | 0.013 |
| Bibliometrics | 0.029 | 0.033 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.015 | 0.009 |
| Research integrity | 0.014 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".