Conducting a Systematic Review and Meta-analysis in Rehabilitation
Bibliographic record
Abstract
ABSTRACT: Systematic reviews are reviews of the literature using a step-by-step approach in a systematic way. Meta-analyses are systematic reviews that use statistical methods to combine the included studies to generate an effect estimate. In this article, we summarize 10 steps for conducting systematic reviews and meta-analyses in the field of rehabilitation medicine: protocol, review team and funding, objectives and research question, literature search, study selection, risk of bias, data extraction, data analysis, reporting of results and conclusions, and publication and dissemination. There are currently 64,958 trials that contain the word "rehabilitation" in CENTRAL (the database of clinical trials in the Cochrane Library), only 1246 reviews, and 237 protocols. There is an urgent need for rehabilitation physicians to engage and conduct systematic reviews and meta-analysis of a variety of rehabilitation interventions. Systematic reviews have become the foundation of clinical practice guidelines, health technology assessments, formulary inclusion decisions and to guide funding additional research in that area.
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 | 0.209 | 0.357 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.028 | 0.036 |
| Bibliometrics | 0.024 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".