Diagnostic Accuracy and Confidence in Management of Forearm and Hand Fractures Among Foundation Doctors in the Accident and Emergency Department: Survey Study
Bibliographic record
Abstract
BACKGROUND: Accurate interpretation of radiographs is crucial for junior doctors in the accident and emergency (A&E) department (the emergency medicine department). However, it remains a significant challenge and a leading cause of diagnostic errors. OBJECTIVE: This study aimed to evaluate the accuracy and confidence of foundation doctors (doctors within their first 2 years of qualifying) in correctly interpreting and managing forearm and hand fractures on plain radiographs. METHODS: A total of 42 foundation doctors with less than 2 years of experience and no prior emergency medicine training who worked in a large district general hospital participated in a web-based questionnaire. The questionnaire consisted of 3 case studies: distal radius fracture, scaphoid fracture, and a normal radiograph. Respondents were required to identify the presence or absence of a fracture, determine the fracture location, suggest appropriate management, and rate their confidence on a Likert scale. RESULTS: Overall, 48% (61/126) of respondents accurately identified the presence and location of fractures. The correct management option was chosen by 64% (81/126) of respondents. The median diagnostic confidence score was 4 of 10, with a mean diagnostic certainty of 4.4 of 10. Notably, respondents exhibited a significantly lower confidence score for the normal radiograph compared to the distal radius fracture radiograph (P=.01). CONCLUSIONS: This study reveals diagnostic uncertainty among foundation doctors in interpreting plain radiographs, with a notable inclination toward overdiagnosing fractures. The findings emphasize the need for close supervision and senior support to mitigate diagnostic errors. Further training and educational interventions are warranted to improve the accuracy and confidence of junior doctors in radiographic interpretation. This study has several limitations, including a small sample size and reliance on self-reported data. The findings may not be generalizable to other health care settings or specialties. Future research should aim for larger, more diverse samples and explore the impact of specific educational interventions on diagnostic accuracy and confidence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".