Using learning-by-concordance to develop reasoning in epistaxis management with online feedback: A pilot study
Bibliographic record
Abstract
BACKGROUND: Epistaxis is a recurring cause for referral to emergency departments. Its management can be complex; hence, it is critical to provide appropriate support to Otolaryngology-Head and Neck Surgery (OHNS) residents to develop clinical reasoning skills to manage such cases. Learning-by-Concordance (LbC) is a recently developed educational tool that encourages learners to think through simulated clinical scenarios. A panel of ENTs provides insightful feedback to residents, reflecting a diversity of opinions about practice. Our study aimed to assess LbC's feasibility and perceived value for training OHNS residents in epistaxis management. METHODS: In this qualitative study, three OHNS surgeons, including two faculty members and one resident, wrote the LbC scenarios. The LbC tool was made available to participants through an online platform. A panel of four OHNS faculty provided feedback on answers to LbC questions. Otolaryngology-Head and Neck Surgery residents participated and provided their opinion on the value of this educational tool through an online questionnaire. RESULTS: A total of 10 one-hour sessions were required to create and upload the training tool. To provide insightful feedback embedded in the learning tool, the four panelists needed 60 min each. Of the 37 participating residents, 25 (68%) completed the training. Overall satisfaction was high: 88% appreciated the training method, and 92% wanted to use this type of training again. Most residents felt the training enabled them to improve their clinical reasoning when encountering a patient with epistaxis (92%) and their knowledge about epistaxis (96%). CONCLUSION: Findings suggest that OHNS residents could benefit from clinical reasoning exercises with panelist feedback using the LbC approach for clinical presentations that require complex approaches to manage conditions such as epistaxis.
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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 | 0.021 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".