Diffusion Tensor Imaging Tractography of the Facial Nerve in Patients With Cerebellopontine Angle Tumors
Bibliographic record
Abstract
OBJECTIVE: To demonstrate the utility of diffusion tensor imaging (DTI) fiber tractography of the facial nerve in patients with cerebellopontine angle (CPA) tumors. STUDY DESIGN: Prospective. SETTING: Tertiary referral center. PATIENTS: DTI technique was established in 113 patients without tumors and in 28 patients with CPA tumors. Subsequently, DTI results were compared with intraoperative findings in 21 patients with medium and large-sized tumors, treated surgically via a translabyrinthine approach. INTERVENTION: Three Tesla magnetic resonance (MR) was used for DTI tractography. For patients without CPA tumors, the scanning protocol was 32 directions with a 3 × 3 × 3 mm voxel size. For CPA tumor patients, scanning protocol was 32 directions with a 2 × 2 × 2 mm voxel size. DTI data were used to track the facial nerve. MAIN OUTCOME MEASURES: Facial nerve identification rate. RESULTS: Facial nerve identification rate in MR-DTI was 97% and 100% in patients without tumors and in patients with tumors of the CPA of the internal auditory canal that were not treated surgically, respectively. MR-DTI identification of the facial nerve was successful in 20 patients who were treated surgically (95%). Good agreement between surgical findings and MR-DTI results was found in 19 patients (90%). CONCLUSION: MR DTI tractography is an effective technique in positively identifying the position of the facial nerve in patients with CPA tumors.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".