Avaliação Psicométrica da Prova de Título de Especialista em Cardiologia da Sociedade Brasileira de Cardiologia
Bibliographic record
Abstract
BACKGROUND: The Cardiology Certification Exam is issued annually by the Brazilian Cardiology Society and set and applied by the Judging Committee for the Cardiologist Title (CJTEC). The psychometric analysis of the exam items using the Item Response Theory (IRT) may provide robust data that can help in the continuous improvement of this instrument. OBJECTIVES: To evaluate the psychometric properties of the 2019 Cardiology Certification Exam in relation to the IR parameters. METHODS: This was an observational study, with psychometric analysis of the 120 questions of the exam taken by 1,120 candidates for the title of Cardiologist in 2019. RESULTS: The IRT analysis revealed that 32.2% of the items had a "high" or "very high" discriminating power, 49.2% were categorized as "easy" or "very easy", and 41.5% showed a high probability of a correct guessing. Sixty-nine deficient items in terms of the IRT parameters were identified, which were then considered poorly effective in evaluating the candidate's ability. CONCLUSIONS: The psychometric analysis of the 2019 Cardiology Certification Exam by the IRT revealed a high percentage of easy questions, with nearly two thirds of the items with a high probability of correct guessing. These data may serve as a basis for a series of discussions and proposals for the elaboration of future certificate exams in Cardiology.
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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 | 0.017 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".