Evaluation of a Novel Case-Based Teaching Series for First Year Otolaryngology – Head & Neck Surgery Residents
Bibliographic record
Abstract
Abstract Introduction First-year resident physicians began training in July 2020 in an environment of decreased clinical case exposure and increased feelings of insecurity secondary to the coronavirus (COVID-19) pandemic. To support resident learning, the University of Toronto Department of Otolaryngology - Head & Neck Surgery piloted a novel virtual case-based discussion series for first-year residents. Methods A weekly virtual resident-led case-based discussion series was designed. In 2020/2021, six residents (“Cohort 1”) and four staff otolaryngologists participated. A Likert survey retrospectively evaluated participant comfort level on a scale of 1 to 5, from “not well prepared or comfortable” to “very well prepared or comfortable” in seven clinical areas, at the beginning of the post-graduate year (PGY-1) August 2020, and in May 2021. Qualitative data collected assessed strengths and weaknesses of the intervention. In July 2021, the new 2021/2022 PGY-1 cohort (“Cohort 2”) also completed cross-sectional surveys in August 2021 and March 2022 to assess their comfort levels with consult management at the two separate timepoints. Results Cases presented revealed areas for practical, systemic, and cultural improvement. With respect to clinical decision making, both “Cohort 1” and “Cohort 2” residents reported increased comfort level in all areas assessed. “Cohort 1” residents reported percentage increase in comfort level addressing all consults of 28%, triaging consults overnight of 24%, pediatric consults of 30%, otology consults of 32%, airway consults of 30%, epistaxis consults of 28%, and peritonsillar abscess consults of 24%. “Cohort 2” residents reported increase in comfort level managing all consults of 32%, triaging consults overnight of 30%, pediatric consults of 38%, otology consults of 2%, airway consults of 30%, epistaxis consults of 18%, and peritonsillar abscess consults of 24%. All respondents agreed the intervention would benefit residents of other programs as a prolonged orientation to residency and a safe and confidential forum to discuss best practices. Discussion A weekly case discussion series potentially improves both resident education and patient care. It facilitated real-time discussion of topics relevant to self-perceived knowledge deficits, timely advice on management of a new and changing population of COVID patients and brought to attention hidden curriculum topics for exploration. Conclusion The case series described could be applied to benefit residents in Otolaryngology and other surgical specialty programs nationwide during, and following, the pandemic.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".