Predicting Microsurgical Aptitude
Bibliographic record
Abstract
OBJECTIVE: Microscopic techniques are an essential part of otolaryngologic practice. These procedures demand advanced psychomotor and visuospatial skills, and trainees possess these abilities to varying degrees. No method currently exists to predict who will possess an aptitude for microscopic surgery. Our goal was to determine whether performance can be predicted by background experiences or skills. STUDY DESIGN: Retrospective cohort study. SETTING: Tertiary academic hospital. SUBJECTS: Students with no previous surgical experience. INTERVENTIONS: Subjects were surveyed on a wide range characteristics thought to affect surgical aptitude, with a primary focus on video gaming and musical training. MAIN OUTCOME MEASURE: Subjects performed a microsurgical task using a novel simulator and their performance was assessed by blinded investigators. RESULTS: Forty-six students were assessed. There was no correlation between video gaming and improved microsurgical performance. Rather, video gamers obtained worse scores, although this difference did not reach significance. The majority of students played a musical instrument. Within this group, musicians who began playing at younger ages obtained higher scores, with the highest scores obtained by musicians who began playing before age 6. However, musicians did not obtain higher scores than non-musicians, regardless of their age of initiation. CONCLUSIONS: No improvement in microsurgical aptitude was seen in subjects who had a history of video gaming or musical instrument playing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".