Evaluation of the methodological quality of systematic reviews of health status measurement instruments
Bibliographic record
Abstract
A systematic review of measurement properties of health-status instruments is a tool for evaluating the quality of instruments. Our aim was to appraise the quality of the review process, to describe how authors assess the methodological quality of primary studies of measurement properties, and to describe how authors evaluate results of the studies. Literature searches were performed in three databases. One hundred and forty-eight reviews were included. The purpose of included reviews was to identify health status instruments used in an evaluative application and to report on the measurement properties of these instruments. Two independent reviewers selected the articles and extracted the data. Reviews were often of low quality: 22% of the reviews used one database, the search strategy was often poorly described, and in many cases it was not reported whether article selection (75%) and data extraction (71%) was done by two independent reviewers. In 11 reviews the methodological quality of the primary studies was evaluated for all measurement properties, and of these 11 reviews only 7 evaluated the results. Methods to evaluate the quality of the primary studies and the results differed widely. The poor quality of reviews hampers evidence-based selection of instruments. Guidelines for conducting and reporting systematic reviews of measurement properties should be developed.
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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 | 0.615 | 0.837 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.026 | 0.028 |
| Bibliometrics | 0.055 | 0.040 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.006 | 0.004 |
| 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".