P.180 What do patients expect of a competent neurosurgeon?
Bibliographic record
Abstract
Background: To improve accountability and reflect patient and societal needs, the Royal College of Physicians and Surgeons of Canada proposed Competence by Design (CBD) for all residency programs. This study compares neurosurgical patient values and expectations of their neurosurgeon to resident competences proposed by CBD curriculum. Methods: Semi-structured interviews of 30 neurosurgical patients and family members were recorded, transcribed and analyzed for themes. Results: Of the first 13 interviews (8 males, 5 females; median age 54), 10 had English as a first language, all completed post-secondary education, and 8 had a brain tumor. In addition to expecting excellent surgical skills and comprehensive medical knowledge, participants expected “good” neurosurgeons to be human (compassionate, empathetic, no ego), transparent communicators, accountable, passionate, collaborative, emotionally composed and highly intuitive. However, there were marked differences in minimum set of competencies required and the expectations of the thresholds to determine competence for neurosurgeons. Conclusions: Patient perspectives show commonalities and marked differences of the expected competencies compared to CBD and significant variability of the thresholds of competence. Further investigations should explore these themes in other specialties. The existing CBD curriculum will need to expand its framework to include humanistic values to improve public perceptions of competence.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".