Real‐time continuous image‐guided surgery: Preclinical investigation in glossectomy
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: To develop, validate, and study the efficacy of an intraoperative real-time continuous image-guided surgery (RTC-IGS) system for glossectomy. STUDY DESIGN: Prospective study. METHODS: We created a RTC-IGS system and surgical simulator for glossectomy, enabling definition of a surgical target preoperatively, real-time cautery tracking, and display of a surgical plan intraoperatively. System performance was evaluated by a group of otolaryngology residents, fellows, medical students, and staff under a reproducible setting by using realistic tongue phantoms. Evaluators were grouped into a senior and a junior group based on surgical experience, and guided and unguided tumor resections were performed. National Aeronautics and Space Administration Task Load Index (NASA-TLX) scores and a Likert scale were used to measure workloads and impressions of the system, respectively. Efficacy was studied by comparing surgical accuracy, time, collateral damage, and workload between RTC-IGS and non-navigated resections. RESULTS: The senior group performed more accurately (80.9% ± 3.7% vs. 75.2% ± 5.5%, P = .28), required less time (5.0 ± 1.3 minutes vs. 7.3 ± 1.2 minutes, P = .17), and experienced lower workload (43 ± 2.0 vs. 64.4 ± 1.3 NASA-TLX score, P = .08), suggesting a trend of construct validity. Impressions were favorable, with participants reporting the system is a valuable practice tool (4.0/5 ± 0.3) and increases confidence (3.9/5 ± 0.4). Use of RTC-IGS improved both groups' accuracy, with the junior group improving from 64.4% ± 5.4% to 75.2% ± 5.5% (P = .01) and the senior group improving from 76.1% ± 4.5% to 80.9% ± 3.7% (P = .16). CONCLUSIONS: We created an RTC-IGS system and surgical simulator and demonstrated a trend of construct validity. Our navigated simulator allows junior trainees to practice glossectomies outside the operating room. In all evaluators, navigation assistance resulted in increased surgical accuracy. LEVEL OF EVIDENCE: NA Laryngoscope, 127:E347-E353, 2017.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".