Critical Hierarchical Appraisal and repOrting tool for composite measureS (CHAOS)
Bibliographic record
Abstract
Background Composite measures are often used to represent certain concepts that cannot be measured with single variables and can be used as diagnoses, prognostic factors, or outcomes in clinical or health research. For example, frailty is a diagnosis confirmed based on the number of age-related symptoms and has been used to predict major health outcomes. However, undeclared assumptions and problems are prevalent among composite measures. Thus, we aim to propose a reporting guide and an appraisal tool for identifying these assumptions and problems. Methods We developed this reporting and assessment tool based on evidence and the consensus of experts pioneering research on index mining and syndrome mining. We designed a development framework for composite measures and then tested and revised it based on several composite measures commonly used in medical research, such as frailty, body mass index (BMI), mental illness diagnoses, and innovative indices mined for mortality prediction. We extracted review questions and reporting items from various issues identified by the development framework. This panel reviewed the identified issues, considered other aspects that might have been neglected in previous studies, and reached a consensus on the questions to be used by the reporting and assessment tool. Results We selected 19 questions in seven domains for reporting or critical assessment. Each domain contains review questions for authors and readers to critically evaluate the interpretability and validity of composite measures, which include candidate variable selection, variable inclusion and assumption declaration, data processing, weighting scheme, methods to aggregate information, composite measure interpretation and justification, and recommendations on the use. Conclusions For all seven domains, interpretability is central with respect to composite measures. Variable inclusion and assumptions are important clues to show the connection between composite measures and their theories. This tool can help researchers and readers understand the appropriateness of composite measures by exploring various issues. We recommend using this Critical Hierarchical Appraisal and repOrting tool for composite measureS (CHAOS) along with other critical appraisal tools to evaluate study design or risk of bias.
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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 | 0.608 | 0.844 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.031 | 0.020 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.009 | 0.016 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".