Needs Assessment for Incoming PGY-1 Residents in Neurosurgical Residency
Bibliographic record
Abstract
BACKGROUND: Residents must develop a diverse range of skills in order to practice neurosurgery safely and effectively. The purpose of this study was to identify the foundational skills required for neurosurgical trainees as they transition from medical school to residency. METHODS: Based on the CanMEDS competency framework, a web-based survey was distributed to all Canadian academic neurosurgical centers, targeting incoming and current PGY-1 neurosurgical residents as well as program directors. Using Likert scale and free-text responses, respondents rated the importance of various cognitive (e.g. management of raised intracranial pressure), technical (e.g. performing a lumbar puncture) and behavioral skills (e.g. obtaining informed consent) required for a PGY-1 neurosurgical resident. RESULTS: Of 52 individuals contacted, 38 responses were received. Of these, 10 were from program directors (71%), 11 from current PGY-1 residents (58%) and 17 from incoming PGY-1 residents (89%). Respondents emphasized operative skills such as proper sterile technique and patient positioning; clinical skills such as lesion localization and interpreting neuro-imaging; management skills for common scenarios such as raised intracranial pressure and status epilepticus; and technical skills such as lumbar puncture and external ventricular drain placement. Free text answers were concordant with the Likert scale results. DISCUSSION: We surveyed Canadian neurosurgical program directors and PGY-1 residents to identify areas perceived as foundational to neurosurgical residency education and training. This information is valuable for evaluating the appropriateness of a training program's goals and objectives, as well as for generating a national educational curriculum for incoming PGY-1 residents.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".