Neurosurgical Knowledge Of Interns In New Zealand: The Potential For Improvement
Bibliographic record
Abstract
Aims: Insufficient exposure of students to neurosurgery and neuroradiology has often been a matter of concern in medical schools across USA, Canada, UK and Europe. When taking into account the high incidence and mortality from head injuries in the form of subarachnoid and intracranial haemorrhages, it becomes evident that core knowledge in basic neurosurgical imaging and diagnoses need to be an essential part of medical training. The aim of this pilot study was to investigate the level of basic neurosurgical knowledge with regard to image interpretation in interns who were in their first postgraduate year in New Zealand. Study Design: Clinical and educational research paper. Place and Duration of Study: Wellington, New Zealand from January 2011 to January 2012. Methodology: Fifty interns in their first postgraduate year were invited to complete a neurosurgical imaging questionnaire with images of common neurosurgical findings (obvious subarachnoid and intracranial haemorrhages) randomly mixed with normal studies. Five computerized tomography (CT) scan images were required to be matched to five diagnoses. Results: All respondents agreed to participate. The mean score for the all 50 interns was 40% (95% CI 37.3 - 42.4), with a range of 0 to 80%. Thirty-six interns (72%) had a score of less than 60% and thus failed to demonstrate basic competency on the examination. None of the interns scored a full 100%. Conclusion: This study suggests that only 28% of newly qualified New Zealand doctors were able to demonstrate a basic level of competence in the evaluation of neurosurgical imaging at the start of their internship. Improvement of the undergraduate neurosurgical curriculum is strongly suggested.
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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 | 0.003 | 0.011 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".