Cognitive Aids in Medicine Assessment Tool (CMAT): preliminary validation of a novel tool for the assessment of emergency cognitive aids
Bibliographic record
Abstract
Applying human factors principles to the design of clinical emergency guidelines is important. The UK Civil Aviation Authority uses a Checklist Assessment Tool for evaluating the content and usability of emergency drills before introduction into service on aircraft. We hypothesised that this model could be used to develop a generic medical tool. A three-stage modified Delphi process was used to adapt the above tool for use in designing medical emergency guidelines. The resulting Cognitive aids in Medicine Assessment Tool was then used to score and rank seven published difficult airway guidelines; the scores were used to assess its validity and reliability. Pearson's rank coefficient between these scores and scores from independent assessors was 0.89 (p = 0.007). Internal consistency, as assessed by Cronbach's alpha, was 0.74, 0.96 and 0.72 for the tool's three constituent domains of physical characteristics, content and layout/format, respectively. Inter-rater reliability, as assessed by Cohen's kappa, ranged from 0.33 to 0.72. The adoption of our tool has the potential to improve the usability of medical emergency guidelines.
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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 | 0.016 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".