The surgical personality: comparisons between urologists, non-urologists
Bibliographic record
Abstract
BACKGROUND: : Our objective was to compare personality traits between urologists and other surgeons, as well as between surgeons and non-surgeons. METHODS: : Eighty-six surgeons (57 faculty, 29 residents) completed the Revised NEO Personality Inventory (NEO PI-R), a validated measure of normal personality traits. Subjects agree or disagree with 240 statements to generate a score in each of the 5 major character traits: extraversion (E), openness (O), conscientiousness (C), agreeableness (A) and neuroticism (N). Each factor is subdivided into 6 component facets. Scores for urology faculty and residents were compared to the other surgical groups and 21 pediatricians. RESULTS: : The 3 groups differed significantly on extraversion (p < 0.001). Post-hoc analyses indicated that urologists obtained significantly higher extraversion scores than other surgeons (p < 0.05) and non-surgeons (p < 0.001) Other surgeons also scored significantly higher than non-surgeons on the extraversion factor (p < 0.05). No significant differences emerged on openness, conscientiousness, agreeableness or neuroticism. Of the 6 extraversion facets, urologists obtained significantly higher scores on gregariousness and excitement-seeking (p < 0.05) than non-urologist surgeons, and significantly higher scores on gregariousness (p < 0.05), activity (p < 0.01), excitement seeking (p < 0.001), and positive emotions (p < 0.05) than non-surgeons. Non-urologist surgeons obtained significantly higher scores than non-surgeons on Activity and Excitement-seeking (p < 0.01).There were no significant differences in the facets of warmth or assertiveness. INTERPRETATION: : Urologists appear to be more extraverted compared to other surgeons. Both groups of surgeons were more extraverted than non-surgeons. If these findings can be confirmed on a wider basis, the data may be helpful in resident selection, mentoring, evaluation and career counselling.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".