Content validation of a Critical Appraisal Tool for Reviewing Analgesia Studies (CATRAS) involving subjects incapable of self-reporting pain
Bibliographic record
Abstract
INTRODUCTION: This article reports the content validation of a Critical Appraisal Tool designed to Review the quality of Analgesia Studies (CATRAS) involving subjects incapable of self-reporting pain and provide guidance as to the strengths and weakness of findings. The CATRAS quality items encompass 3 domains: level of evidence, methodological soundness, and grading of the pain assessment tool. OBJECTIVES: To validate a critical appraisal tool for reviewing analgesia studies involving subjects incapable of self-reporting pain. METHODS: Content validation was achieved using Delphi methodology through panel consensus. A panel of 6 experts reviewed the CATRAS in 3 rounds and quantitatively rated the relevance of the instrument and each of its quality items to their respective domains. RESULTS: Content validation was achieved for each item of the CATRAS and the tool as a whole. Item-level content validity index and kappa coefficient were at least greater than 0.83 and 0.81, respectively, for all items except for one item in domain 2 that was later removed. Scale-level content validity index was 97% (excellent content validity). CONCLUSIONS: This 67-item critical appraisal tool may enable critical and quantitative assessment of the quality of individual analgesia trials involving subjects incapable of self-reporting pain for use in systematic reviews and meta-analysis studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.551 | 0.760 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.023 | 0.014 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".