Quality of reporting of economic evaluations in rehabilitation research: a systematic review
Bibliographic record
Abstract
BACKGROUND: The quality of reporting of health economic evaluations for rehabilitation services has been questioned, limiting the ability to provide accurate recommendations for health decisions. PURPOSE: To document current overall reporting quality of the published literature for economic evaluations of rehabilitation services using the Consolidated Health Economic Evaluation Reporting Standards (CHEERS), and to identify factors that could influence the quality of reporting. DATA SOURCES: the Cochrane Library. STUDY SELECTION: Prospective rehabilitation economic evaluation articles from 2013 to 2020 were selected. DATA EXTRACTION: Data were extracted by one reviewer and independently verified by a second reviewer. DATA SYNTHESIS/RESULTS: Title and abstracts of 3,454 papers were reviewed. 204 papers were selected for a full text screening. From those, 129 potential papers were identified to be included in this study. LIMITATIONS: Only two databases were used in data collection, and papers were selected from 2013 to 2020 only. CONCLUSIONS: Inconsistent reporting in health economic evaluations of rehabilitation services has continued, despite the availability of the CHEERS checklist. The methods of the analyzed studies were frequently under-reported, thereby creating challenges in determining whether the results reported were valid.IMPLICATIONS FOR REHABILITATIONVariable quality of reporting has been identified in rehabilitation research assessing cost-effectiveness.To grow as an area of expertise, the field of rehabilitation must produce research demonstrating its cost-effectiveness.Both rehabilitation clinicians and funders would benefit from full and transparent information to identify optimal solutions for effective and efficient care.
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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 | 0.502 | 0.831 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.019 | 0.027 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".