Choosing a Computer-Assisted Surgical System for Sinus Surgery
Bibliographic record
Abstract
OBJECTIVES: Choosing the correct computer-assisted system for sinus surgery can be a formidable task for surgeons and their institutions. A formal trial of an electromagnetic (InstaTrak, Visualization Technologies Incorporated, Woburn, MA) and an optical (LandmarX by Medtronic-Xomed, Jacksonville, FL) system was conducted over a 10-month period at the St. Paul's Sinus Centre in Vancouver, BC. An objective and subjective evaluation method was used to select the system for use at our institution. METHODS: Thirty-nine patients were operated on by the senior author (A.R.J.) using the InstaTrak (23 patients) or the LandmarX (16 patients). The two groups were balanced in terms of age, gender, number of previous surgeries, and extent of surgery. Estimated blood loss, surgical time, and surgical complications were compared between the groups, with all other variables constant. Nursing and radiology personnel as well as the three surgeons who used both systems at the centre were surveyed on patient safety, ease of use, storage, and accessories. RESULTS: There was no statistical difference in objective surgical data. There was a more pronounced learning curve using the LandmarX as defined by a greater decrease in the duration of surgical time by the end of the trial period. The operating room personnel found the InstaTrak easier to use, whereas the computed tomography technologists preferred the LandmarX. The surgeons found the two systems acceptable in terms of navigational accuracy; however, the InstaTrak was felt to be more user friendly. CONCLUSIONS: The InstaTrak system was chosen for our institution mainly because of its ease of use by the operating room staff and the sinus surgeons.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".