Spatial visualization ability and laparoscopic skills in novice learners: Evaluating stereoscopic versus monoscopic visualizations
Bibliographic record
Abstract
Elevated spatial visualization ability (Vz) is thought to influence surgical skill acquisition and performance. Current research suggests that stereo visualization technology and its association with skill performance may confer perceptual advantages. This is of particular interest in laparoscopic skill training, where stereo visualization may confer learning advantages to novices of variant Vz. This study explored laparoscopic skill performance scores in novices with variable spatial ability utilizing stereoscopic and traditional monoscopic visualization paradigms. Utilizing the McGill Inanimate System for Teaching and Evaluating Laparoscopic Skills (MISTELS) scoring protocol it was hypothesized that individuals with high spatial visualization ability (HVz) would achieve higher overall and individual MISTELS task scores as compared to low spatial visualization ability (LVz) counterparts. Further, we also hypothesized that a difference would exist between HVz and LVz individual scores based on the viewing modality employed. No significant difference was observed between HVz and LVz individuals for MISTELS tasks scores, overall or individually under both viewing modalities, despite higher average MISTELS scores for HVz individuals. The lack of difference between scores obtained under the stereo modality suggested that the additional depth that is conferred by the stereoscopic visualization may act to enhance performance for individuals with LVz, potentially equilibrating their performance with their HVz peers. Further experimentation is required to better ascertain the effects of stereo visualization in individuals of high and low Vz, though it appears stereoscopic visualizations could serve as a prosthetic to enhance skill performance.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".