A quality assessment of systematic reviews on telerehabilitation: what does the evidence tell us?
Bibliographic record
Abstract
AIMS: To evaluate the quality of systematic reviews on telerehabilitation. METHODS: The AMSTAR--Assessment of Multiple Systematic Reviews--checklist was used to appraise the evidence related to the systematic reviews. RESULTS: Among the 477 records initially identified, 10 systematic reviews matched the inclusion criteria. Fifty percent were of high quality; anyway the majority of them did not report the following aspects: i) analysis of the grey literature; ii) a list of the excluded studies and their characteristics; iii) the identification of possible source of bias and the assessment of its likehood; iv) an appropriate method to combine the findings of the included studies addressing the heterogeneity as well. From the main findings of the high-scored systematic reviews telerehabilitation resulted at least as effective as usual care: 1) in the short term treatment of mental health related to people affected by spinal cord injury; 2) in rural communities for treating patients affected by chronic conditions; 3) in treating common pathologies (mainly asthma) affecting children and adolescents. As for stroke, evidence is currently insufficient to reach conclusions about its effectiveness. As for costs, there is insufficient evidence to confirm that telerehabilitation is a cost-saving or cost-effective solution. CONCLUSIONS: In the authors' knowledge this is the first attempt to evaluate the quality of systematic reviews on telerehabilitation. This work also identified the main findings related to the high-scored systematic reviews; the analysis confirms that there is a mounting evidence concerning the effectiveness of telerehabilitation, at least for some pathologies.
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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 | 0.410 | 0.681 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.024 | 0.031 |
| Bibliometrics | 0.048 | 0.034 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".