Growing Vestibular Schwannomas
Bibliographic record
Abstract
OBJECTIVE: To determine the subsequent growth patterns in vestibular schwannomas shown to be growing on serial imaging. STUDY DESIGN: Retrospective review. SETTING: Tertiary academic referral center. PATIENTS: Patients with tumors that demonstrated growth of greater than 1 mm/yr between 2 consecutive scans (magnetic resonance or computed tomography) and had at least 1 follow-up scan were included. Patients with neurofibromatosis were excluded. INTERVENTION(S): Review of radiographic images (magnetic resonance imaging scans) or neuroradiologists' reports when the images were unavailable. MAIN OUTCOME MEASURE(S): Maximum dimension along the axis of the internal auditory canal was measured for intracanalicular tumors, whereas for extracanalicular tumors, maximal dimension along any orientation was used. A significant difference in dimension was defined to be greater than 1 mm/yr, positive or negative. RESULTS: Thirty-six patients were included in the study; 47% were women and with an average age of 60.2 years. The average follow-up period after growth was identified was 2.1 years. Of the growing tumors, 63.9% continued to grow, 30.6% stayed the same size, and 5.6% regressed in size. CONCLUSION: Most vestibular schwannomas identified to be growing are likely to continue to grow on subsequent serial imaging. These results are useful in clinical decision making and counseling patients with growing vestibular schwannomas.
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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 | 0.003 |
| 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) | 0.002 | 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".