Long-Term Quality of Life Outcomes across a Matched Series Undergoing Radiation, Surgery, and Active Surveillance in Acoustic Neuroma Patients
Bibliographic record
Abstract
Introduction: Current literature on acoustic neuroma (AN) management is saturated with outcomes examining tumor progression and morbidity. However, for the clinician deciding between aggressive or conservative treatment approaches for these benign slow growing tumors, equal consideration of both long-term tumor progression and patient quality of life is paramount. We present the first study that compares a decade-long progression of quality of life across radiation, surgery and active surveillance treatment approaches. Methods: Retrospective review of all patients undergoing stereotactic radiation (either radiosurgery or radiotherapy) (SR), surgical resection and active surveillance for AN at a single tertiary center between 2000 and 2017 was conducted to gather radiological records, tumor characteristics, clinical presentation, symptoms, recurrence, mortality and pre and post-treatment patient reported quality of life outcomes. SR patients were matched for size and age with the surgical and active surveillance group. Quality of life (QoL) was measured using the 36-Item Short Form Health Survey (SF-36) at baseline pre and posttreatment time points for SR and surgical groups. SF-36 was obtained at baseline and at last follow-up for the surveillance group. Further prospective collection of long-term symptom and SF-36 scores was performed for final long-term follow-up. Results: Between 2000 and 2017, a total of 114 SR patients, 630 surgical patients, and 133 active surveillance patients were identified. 1:1 matching was conducted between the three groups to control for age at diagnosis, tumor size, hearing status at baseline and duration of follow-up. Individual SF-36 score components and summary components were compared, generating a 12-year outlook on QoL in AN patients. Conclusion: To our knowledge, we report on the largest long-term series of quality of life outcomes using SF-36, comparing SR, surgery, and active surveillance in management of AN. Publication History Article published online: 05 February 2024 © 2024. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".