Systematic reviews in spinal cord injury: A step-by-step guide for rehabilitation science learners and clinicians
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has created opportunity for multiple rehabilitation science learners and clinicians to critically evaluate and synthesize published research in the field of spinal cord injury (SCI) rehabilitation. OBJECTIVE: To provide a step-by-step guide for rehabilitation science learners and clinicians outlining how to conduct rigorous systematic reviews in the field of SCI. RESULTS: Steps for conducting a systematic review (SR) include: (1) formation of the SR team consisting of interprofessional experts; (2) formulation of the research question(s) with patient/population/problem, Intervention, Comparison, and Outcome (PICO) specification; (3) determination of inclusion and exclusion criteria; (4) development of SR protocol and registration; (5) development of the search strategies (database specific); (6) screening of titles and abstracts (level 1 screening), and full-texts (level 2 screening); (7) quality assessment of the included studies; (8) data extraction; (9) summary of findings and discussion; and, (10) dissemination of results. CONCLUSIONS: The enclosed ten steps for conducting SRs in SCI rehabilitation research have the potential to significantly improve the quality of evidence synthesis and the associated inferences. The importance of assembling team with diverse expertise is emphasized to assure a quality product with the potential to influence practice and inform the content of clinical practice recommendations.
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 | 0.171 | 0.344 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.011 | 0.011 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.021 | 0.019 |
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".