Can stereoscopic technologies build better surgeons? The effects of stereoscopy and spatial visualization ability on laparoscopic performance in surgical residents (536.12)
Bibliographic record
Abstract
Acquisition of surgical skill is a multifaceted process, in which practice, and innate human factors such as spatial visualization ability (Vz) play key roles. Elevated Vz (HVz) is thought to facilitate surgical skill acquisition. The benefit of viewing laparoscopic skills in stereo (3D) is undetermined in experienced populations, and may show differing effects between those of variable Vz. The Vz of surgical residents will be evaluated using a Mental Rotations Test, and then surgical skill will be measured via the McGill Inanimate System for Training and Evaluation of Laparoscopic Skills (MISTELS). Participants will complete the MISTELS tasks in both 3D and monoscopic format (2D) while being randomly assigned to complete the task in either 2D first or 3D first. Scoring of the MISTELS is based on time, completion and efficiency. It is hypothesized that HVz individuals will score higher than LVz counterparts. Prior research in novice subjects suggests that the effects of 3D and 2D viewing yields variable results based on an individual’s Vz, such that LVz trainees may benefit more than HVz from 3D viewing after a period of experience. If 3D viewing enhances surgical skill in our subjects, it may suggest future use in laparoscopic training curricula. Comparison of current results with those of novice subjects may suggest the intervening effects of experience on the effect of Vz and/or viewing modality.
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 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".