Application Accuracy of an Electromagnetic Field-Based Image-Guided Navigation System
Bibliographic record
Abstract
OBJECTIVE: We tested the application accuracy of an electromagnetic field-based image guidance system to compare it to traditional optically tracked systems. METHODS: A plastic skull phantom was fitted with fiducial markers rigidly attached via self-drilling bone screws. Volumetric CT scan was obtained to simulate the clinical condition. A metal disc marked in 1-mm increments was placed at the expected target point. Following registration and alignment of a trajectory guide, radial and depth localization errors were measured after both freehand and stabilized approaches on both the right and left sides. Statistical analyses of the localization errors were performed. RESULTS: Total target localization error ranged from 0.71 to 3.51 mm with a mean +/- SEM of 2.13 +/- 0.11 mm. The radial error averaged 0.98 +/- 0.11 mm, depth error 1.74 +/- 0.13 mm. The freehand procedures produced a statistically greater radial, depth and total error than the fixed procedures. CONCLUSIONS: Accuracy of image-guided localization using an electromagnetic field guidance system is similar to that reported for optically guided systems.
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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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".