The COSMIN checklist for assessing the methodological quality of studies on measurement properties of health status measurement instruments: an international Delphi study
Bibliographic record
Abstract
BACKGROUND: Aim of the COSMIN study (COnsensus-based Standards for the selection of health status Measurement INstruments) was to develop a consensus-based checklist to evaluate the methodological quality of studies on measurement properties. We present the COSMIN checklist and the agreement of the panel on the items of the checklist. METHODS: A four-round Delphi study was performed with international experts (psychologists, epidemiologists, statisticians and clinicians). Of the 91 invited experts, 57 agreed to participate (63%). Panel members were asked to rate their (dis)agreement with each proposal on a five-point scale. Consensus was considered to be reached when at least 67% of the panel members indicated 'agree' or 'strongly agree'. RESULTS: Consensus was reached on the inclusion of the following measurement properties: internal consistency, reliability, measurement error, content validity (including face validity), construct validity (including structural validity, hypotheses testing and cross-cultural validity), criterion validity, responsiveness, and interpretability. The latter was not considered a measurement property. The panel also reached consensus on how these properties should be assessed. CONCLUSIONS: The resulting COSMIN checklist could be useful when selecting a measurement instrument, peer-reviewing a manuscript, designing or reporting a study on measurement properties, or for educational purposes.
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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 | 0.597 | 0.541 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.020 | 0.008 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.004 | 0.005 |
| 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".