Clinical Experience with Angulated, Hand-Activated, Wireless Instruments in an Optical Tracking System for Endoscopic Sinus Surgery
Bibliographic record
Abstract
OBJECTIVES: Image-guided surgery of the paranasal sinuses has become a valuable tool in endoscopic sinus surgery. Optical image-guided systems using infrared tracking technology are widely used. We present our experience with new angulated, hand-activated, wireless instruments in an optical tracking system for endoscopic sinus surgery. DESIGN: Case series. SETTING: Community university teaching hospital. METHODS: Sixty-five consecutive patients underwent computer-assisted endoscopic sinus surgery using a Stryker Navigation System (Stryker Canada LP, Burlington, ON). Patients underwent preoperative fine-cut axial computed tomography. At the time of surgery, anatomic fiducials were registered and the measured fiducial registration error (FRE), which is an indicator of the accuracy of the optical system, was recorded. Angulated battery-powered active instrumentation was used during the surgery. MAIN OUTCOME MEASURES: FRE, the number of anatomic fiducials used, complication rates pre- and postuse of computer-assisted sinus surgery. RESULTS: The mean FRE was 2.02 +/- 0.48 mm. The mean number of anatomic fiducials used for registration was 5.98. There were no major orbital or intracranial complications. Use of angulated instruments rarely caused a line of sight problem. CONCLUSIONS: The new optical system has a measured FRE comparable to that of other image guidance systems. Our clinical experience shows that the instrumentation decreases the "line of sight" problem and is easy to navigate and manipulate without a wire attachment to the main computer.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".