P.134 Long-term quality of life deterioration in vestibular schwannoma patients treated with radiotherapy: a matched cohort with surveillance, radiation and surgery
Bibliographic record
Abstract
Background: There is an absence of studies evaluating longitudinal quality of life (QoL) amongst vestibular schwannomas (VS) with matched tumor and patient characteristics between treatment groups. We present novel findings of 12 yearlong follow-up of patient QoL and symptomatology outcomes in this matched cohort study. Methods: Symptomatology and 36-Item Short Form Health Survey (SF36) between 2000-2017 in VS patients managed at a single tertiary centre was conducted. Radiation (R) and active surveillance (A) groups were matched for tumor size and age against the surgery (S) group. Results: 14 A patients, 24 R patients, 49 S patients met matching and inclusion criteria. Mean age, tumor diameter and follow up was 69.1 years, 21.6mm and 12.0 years respectively. Mental component summary (MCS) scores deteriorated significantly in the radiation group (3.1 S, 3.7 A, -3.5 R, p-value 0.008). Physical component summary scores remained stable at follow up (-0.2 S, 0.00 S, -4.0 R, p-value 0.227). Various symptoms resolved statistically in surgery goup, whereas tinnitus on follow up was higher with radiation (40.8% S, 66.7% R, p-value 0.038). Conclusions: Surgery group demonstrated improvements in long term QoL with good symptom resolution, whilst radiation group demonstrated small but significant deterioration over time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".