Machine learning to extract communication and history‐taking skills in OSCE transcripts
Bibliographic record
Abstract
OBJECTIVES: Observed Structured Clinical Exams (OSCEs) allow assessment of, and provide feedback to, medical students. Clinical examiners and standardised patients (SP) typically complete itemised checklists and global scoring scales, which have known shortcomings. In this study, we applied machine learning (ML) to label some communication skills and interview content information in OSCE transcripts and to compare several ML methodologies by performance and transferability. METHODS: One-hundred and twenty-one transcripts of two OSCE scenarios were manually annotated per utterance across 19 communication skills and content areas. Utterances were converted to two types of numeric sentence vector representations and were paired with three types of ML algorithms. First, ML models (MLMs) were evaluated using a five K-fold cross-validation technique on all transcripts in one scenario to generate precision and recall, and their harmonic mean, F1 scores. Second, ML models were trained on all 101 transcripts from scenario 1 and tested for transferability on 20 scenario 2 transcripts. RESULTS: Performance testing in the K-fold cross-validation demonstrated relatively high mean F1 scores: median 0.87 and range 0.53-0.98 across all 19 labels. Transferability testing demonstrated success: F1 median 0.76 and range 0.46-0.97. The combination of a bi-directional long short-term memory neural network (biLSTM) algorithm with GenSen numeric sentence vector representations was associated with greater F1 scores across both performance and transferability (P < .005). CONCLUSIONS: We report the first application of ML in the context of student-SP OSCEs. We demonstrated that several MLMs automatically labelled OSCE transcripts for a range of interview content and some clinical communications skills. Some MLMs achieved greater performance and transferability. Optimised MLMs could provide automated and accurate assessment of OSCEs with potential to track student progress and identify areas for further practice.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".