Sex differences in spatial abilities of medical graduates entering residency programs
Bibliographic record
Abstract
Sex differences favoring males in spatial abilities have been known by cognitive psychologists for more than half a century. Spatial abilities have been related to three-dimensional anatomy knowledge and the performance in technical skills. The issue of sex differences in spatial abilities has not been addressed formally in the medical field. The objective of this study was to test an a priori hypothesis of sex differences in spatial abilities in a group of medical graduates entering their residency programs over a five-year period. A cohort of 214 medical graduates entering their specialist residency training programs was enrolled in a prospective study. Spatial abilities were measured with a redrawn Vandenberg and Kuse Mental Rotations Tests in two (MRTA) and three (MRTC) dimensions. Sex differences favoring males were identified in 131 (61.2%) female and 83 (38.8%) male medical graduates with the median (Q1, Q3) MRTA score [12 (8, 14) vs. 15 (12, 18), respectively; P < 0.0001] and MRTC score [7 (5, 9) vs. 9 (7, 12), respectively; P < 0.0001]. Sex differences in spatial abilities favoring males were demonstrated in the field of medical education, in a group of medical graduates entering their residency programs in a five-year experiment. Caution should be exerted in applying our group finding to individuals because a particular female may have higher spatial abilities and a particular male may have lower spatial abilities.
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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) | 0.003 | 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".