P.150 Spontaneous regression of vestibular schwannoma: a clinical and radiographic characterization
Bibliographic record
Abstract
Background: Vestibular schwannoma (VS) are the most common tumour of the CPA with an annual incidence of 17.4/1 million. They typically demonstrate slow growth over time and as such, observation is a reasonable approach to management. A portion of these tumour remain static and approximately 5-10% of these tumours will demonstrate spontaneous regression while under observation, including those associated with neurofibromatosis type-2. Previous case series (N= 13-14) have attempted to identify predictive factors for tumour growth and regression, but few have reached significance or demonstrated reproducible findings. Methods: Using a clinical database of VS treated by one team at our institution, we identified 40 patients who have demonstrated significant spontaneous regression or complete resolution of their VS. All patients received a survey by mail and telephone. Results: Radiographic descriptions were collected on 40 patients. Surveys were completed by 18 participants and an additional 18 control patients who demonstrated growth and underwent surgical resection. Conclusions: This is the largest case series we know of to date describing radiographic and clinical presentations of patients shrinking vestibular schwannoma. It is also the only study known to date to consider patient factors by survey in an attempt to identify protective factors.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".