3‐D Arthroscopy: A New Frontier in Surgical Visualization
Bibliographic record
Abstract
At present, support of 3‐D visualization for laparoscopic surgical procedures is growing. This is likely due to the ability to interpret anatomical structures during the 3‐D visualization previously impossible in 2‐D endoscopic procedures. With the ability to interpret depth cues accurately, surgeons better orient themselves in body cavities with greater ease and fewer movements, ultimately correlating to reduced patient morbidity. Considering the validated success of 3‐D in endoscopy, it is surprising that this technology has not been applied to arthroscopy; the endoscopy of joints. At present, millions of arthroscopic surgeries are performed using primitive 2‐D scope technology on the 3‐D anatomy of the joint cavity, posing a significant conceptual roadblock for novice orthopedic surgeons. This study aims to apply technology used in 3‐D laparoscopes to build a 3‐D arthroscope prototype to enhance operative precision via accurate visualization of the joint. This study also aims to test the efficacy of a prototype for skill acquisition via the Global Ratings Scale for surgical skills, completion time measurements, and instances of perceptual depth errors. It is hypothesized that testing surgical applications of this 3‐D visualization in trainees will result in reduced training time, fewer perceptual errors, increased operative precision and decreased burden of cognitive load in the user. Grant Funding Source : Departmental
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".