P.015 Spontaneous regression of acoustic schwannomas: a predictive model
Bibliographic record
Abstract
Background: Vestibular schwannomas are the most common tumour of the CPA with an annual incidence of 17.4/1 million. Approximately 5-10% of these tumours demonstrate spontaneous regression without intervention while under observation. Previous research studies have assessed patient factors and imaging characteristics through chart review to attempt to identify predictive factors of spontaneous regression. There have not been any studies where patient questionnaires are used to assess patient lifestyle factors or characteristics which may predict spontaneous regression. Methods: Using a clinical database of acoustic schwannomas treated by one team at our institution, we have identified approximately 40 patients, of a database of 900 patients, who have demonstrated significant spontaneous regression (>5mm in size reduction in one dimension) or complete resolution of their acoustic schwannoma. Clinical, radiological, and lifestyle factors are reviewed though clinical records and patient questionnaire. Regression analysis is performed. Results: Using patients who have tumors with significant spontaneous regression, we attempt to create a model that predicts regression of these tumours. Conclusions: In conclusion, this is the first study to consider patient lifestyle factors obtained through patient survey in addition to clinical and radiographic factors to attempt to create a predictive model of spontaneous regression of acoustic schwannoma.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".