Drilling Simulated Temporal Bones with Left-Handed Tools: A Left-Hander's Right?
Bibliographic record
Abstract
OBJECTIVES: Left-handed trainees can be at a disadvantage in the surgical environment because of a right-handed bias. The effectiveness of teaching left-handed trainees to use an otologic drill designed for their dominant hand versus the conventional right-handed drill was examined. METHODS: Novice medical students were recruited from the university community. Twenty-four subjects were left-handed, and 12 were right-handed. Eight left-handed surgeons also participated. A randomized controlled trial was conducted to compare the performance of left-handed trainees using novel left-handed drills to that of left-handed trainees using right-handed tools and to that of right-handed trainees using right-handed tools. The evaluation consisted of 3 phases: pretest, skill acquisition, and 2 post-tests. The measurement tools included expert assessment of performance, and subjective and objective final product analyses. RESULTS: An initial construct validity phase was conducted in which validity of the assessment tools was ensured. Both the left-handers using left-handed tools and the right-handers using right-handed tools significantly outperformed the left-handers using right-handed tools at pretest, immediate posttest, and delayed posttest. All participants improved their performance as a function of practice. CONCLUSIONS: The left-handed trainees learned bone drilling better with tools designed for the left hand. These tools may be incorporated into residency training programs for the development of surgical technical skills. Future studies should assess skill transfer between the left-handed and right-handed drills.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".