Cognitive load and processes during chest radiograph interpretation in the emergency department across the spectrum of expertise
Bibliographic record
Abstract
BACKGROUND: In the emergency department (ED), chest radiographs (CXRs) provide essential information for clinical diagnostic reasoning. Errors in interpretation by emergency physicians can lead to negative patient outcomes. To aid in teaching this important skill, an understanding of cognitive processes and cognitive load (CL) in CXR interpretation in emergency medicine (EM) personnel is warranted. METHODS: This study adopted a concurrent mixed-methods research design. Participant groups included medical students (M), junior (J) and senior (S) EM residents, and attending emergency physicians (P) in the ED at an academic hospital. To elucidate cognitive processes, a real-time cognitive task analysis during CXR interpretation was performed. Interviews were audio recorded, transcribed verbatim, and analyzed thematically. The interview was followed by a questionnaire, where participants rated their CL, stress, and confidence level. RESULTS: = 0.003) as experience level increased. Qualitative analysis of interviews revealed four themes: checking behavior, information reduction, pattern recognition versus systematic viewing, and recognizing scope of practice. Experts commonly utilized checking behavior (e.g., comparison to prior radiographs) and deprioritized task irrelevant data. Experts used a general overview technique as their initial approach as opposed to a systematic viewing approach, and they more readily recognized an EM physicians' scope of practice in this task. CONCLUSION: This study characterized differences in cognition that led to increased CL, stress, and lower level of confidence in EM learners during CXR interpretation and provided insight into expertise development in this important skill.
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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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".