Surgical exploration and discovery program: Inaugural involvement of otolaryngology – head and neck surgery
Bibliographic record
Abstract
BACKGROUND: There is significant variability in undergraduate Otolaryngology - Head and Neck Surgery (OTOHNS) curricula across Canadian medical schools. As part of an extracurricular program delivered jointly with other surgical specialties, the Surgical Exploration and Discovery (SEAD) program presents an opportunity for medical students to experience OTOHNS. The purpose of this study is to review the participation and outcome of OTOHNS in the SEAD program. METHODS: The SEAD program is a two-week, 80-hour, structured curriculum that exposes first-year medical students to nine surgical specialties across three domains: (1) operating room observerships, (2) career discussions with surgeons, and (3) simulation workshops. During observerships students watched or assisted in surgical cases over a 4-hour period. The one-hour career discussion provided a specialty overview and time for students' questions. The simulation included four stations, each run by a surgeon or resident; students rotated in small groups to each station: epistaxis, peritonsillar abscess, tracheostomy, and ear examination. Participants completed questionnaires before and after the program to evaluate changes in career interests; self-assessment of knowledge and skills was also completed following each simulation. Baseline and final evaluations were compared using the Wilcoxon Signed-Rank test. RESULTS: SEAD participants showed significant improvement in knowledge and confidence in surgical skills specific to OTOHNS. The greatest knowledge gain was in ear examination, and greatest gain in confidence was in draining peritonsillar abscesses. The OTOHNS session received a mean rating of 4.8 on a 5-point scale and was the most popular surgical specialty participating in the program. Eight of the 18 participants were interested in OTOHNS as a career at baseline; over the course of the program, two students gained interest and two lost interest in OTOHNS as a potential career path, demonstrating the potential for helping students refine their career choice. CONCLUSIONS: Participants were able to develop OTOHNS knowledge and surgical skills as well as refine their perspective on OTOHNS as a potential career option. These findings demonstrate the potential benefits of OTOHNS departments/divisions implementing observerships, simulations, and career information sessions in pre-clerkship medical education, either in the context of SEAD or as an independent initiative.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".