DESCRIPTION AND VALIDATION OF A MAGNETIC RESONANCE IMAGING‐GUIDED STEREOTACTIC BRAIN BIOPSY DEVICE IN THE DOG
Bibliographic record
Abstract
A stereotactic brain biopsy system that is magnetic resonance (MR) imaging-guided has not been validated in dogs. Our purpose was to determine the mean needle placement error in the caudate nucleus, thalamus, and midbrain of a canine cadaver brain using the modified Brainsight stereotactic system. Relocatable reference markers (fiducial markers) were attached to the cadaver head using a dental bite block. A T1-weighted gradient echo three-dimensional (3D) sequence was acquired using set parameters. Fiducial markers were used to register the head to the acquired MR images in reference to a 3D position sensor. This allowed the planning of trajectory path to brain targets in real time. Coordinates (X, Y, Z) were established for each target and 0.5 microl of diluted gadolinium was injected at each target using a 26-gauge needle to create a lesion. The center of the gadolinium deposition was identified on the postoperative MR images and coordinates (X', Y', Z') were established. The precision of this system in bringing the needle to target (needle placement error) was calculated. Seventeen sites were targeted in the brain. The mean needle placement error for all target sites was 1.79 +/- 0.87 mm. The upper bound of error for this stereotactic system was 3.31 mm. There was no statistically significant relationship between needle placement error and target depth (P = 0.23). The ease of use and precision of this stereotactic system support its development for clinical use in dogs with brain lesions > 3.31 mm.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".