Evaluation of a CAT Database and Expert Appraisal of CATs Developed by Students
Bibliographic record
Abstract
Five steps have been recommended to provide evidence-based patient care: formulating a clinical question, searching for literature, evaluating the validity and applicability of results, implementing results into practice, and assessing if the new evidence has led to improved health care. Students can be trained in these steps by the development of knowledge summaries such as critically appraised topics (CATs). The aim of the present project was the development, use, and evaluation of a German-language CAT database and an appraisal of the quality of CATs developed by students. A total of 153 fifth-year veterinary medical students (in 21 groups) were enrolled in the project. Each group developed a CAT and most students participated in a survey. To learn more about the quality of the CATs, we asked experts to appraise the texts written by the students. The CATs were indexed with key words and assigned to specific fields corresponding to the European Colleges of Veterinary Specialisation. Currently, 57 CATs have been developed. The majority of students stated that writing CATs is a good exercise and that "it is important to teach the assessment of scientific information." In total, 13 experts completed the questionnaires, out of which 9 graded the CAT they appraised as good. In addition to English-language CAT databases, German tools should also be available for students and practitioners.
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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 | 0.315 | 0.496 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.022 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".