Spatial abilities of medical graduates and choice of residency programs
Bibliographic record
Abstract
Spatial abilities have been related in previous studies to three-dimensional (3D) anatomy knowledge and the performance in technical skills. The objective of this study was to relate spatial abilities to residency programs with different levels of content of 3D anatomy knowledge and technical skills. The hypothesis was that the choice of residency program is related to spatial abilities. A cohort of 210 medical graduates was enrolled in a prospective study in a 5-year experiment. Spatial abilities were measured with a redrawn Vandenberg and Kuse Mental Rotations Test (MRT) in two (MRTA) and three (MRTC) dimensions. Medical graduates were enrolled in Family Medicine (n = 76, 36.2%), Internal Medicine (64, 30.5%), Surgery (52, 24.8%), and Anesthesia (18, 8.6%). The assumption was that the level of 3D anatomy knowledge and technical skills content was higher in Surgery and Anesthesia compared to Family Medicine and Internal Medicine. Mean MRTA score of 12.4 (±SD 4.6), 12.0 (±4.3), 14.1 (±4.3), and 14.6 (±4.0) was obtained in Family Medicine, Internal Medicine, Surgery, and Anesthesia, respectively (P = 0.0176). Similarly, mean MRTC score of 8.0 (±4.4), 7.5 (±3.6), 8.5 (±3.9), and 7.9 (±4.1) was obtained (P = 0.5647). Although there was a tendency for lower MRTA score in Family Medicine and Internal Medicine compared to Surgery and Anesthesia, no statistically significant main effect of residency, year, sex, or the interactions were observed for the MRTA and MRTC. Studied sample of medical graduates was not found to choose their residency programs based on their innate 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 | 0.008 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".