Training and Assessment of CXR/Basic Radiology Interpretation Skills: Results From the 2005 CDIM Survey
Bibliographic record
Abstract
BACKGROUND: Despite published literature demonstrating deficiencies in chest radiograph (CXR)/basic radiology interpretation skills of 4th-year medical students, studies and subsequent curricula regarding the training needed to obtain these skills are lacking. Terms such as clinical exposure and radiology teaching have been used to describe the experience for these basic interpretive skills, but best practice methods of delivery, let alone common methods, have yet to be defined. PURPOSE: The objective is to describe the current methods of teaching and assessing CXR/basic radiology interpretation skills across institutions on the 3rd-year internal medicine (IM) clerkship. METHODS: In 2005, the Clerkship Directors in Internal Medicine (CDIM), an international organization representing U.S. and Canadian medical schools, surveyed its institutional members. Twelve questions on the survey dealt with X-ray interpretation. RESULTS: Eighty-eight of 109 members (81%) responded to the survey. Overall, 81% of respondents felt that CXR interpretation is an important clinical skill for medical students. Seventy-six percent indicated that instruction in these skills occurs on the IM clerkship. The most cited methods of instruction were lectures (56%) and teaching rounds (48%). Most schools spent on average of 2 to 4 hr during the IM clerkship on formal radiology instruction. Only 33% indicated that radiology interpretation skills are assessed during the clerkship. The most common assessment methods were written examination (19%) and OSCE (19%). CONCLUSION: Substantive data regarding attainment and assessment of CXR/basic radiology interpretation skills in the undergraduate curriculum are lacking. Our study provides preliminary descriptive data regarding CXR instruction and assessment on the 3rd-year IM clerkship.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".