Optimal dose of stereotactic radiosurgery for acoustic neuromas: a systematic review
Bibliographic record
Abstract
Radiosurgery is increasingly employed in the treatment of acoustic neuroma, but the optimal dose in terms of long-term tumour control and minimal adverse effects has not been established. We performed a systematic review of the published literature of radiosurgery of acoustic neuroma to assess whether the use of low dose radiosurgery is as effective as high dose treatment. Reports of radiosurgery for acoustic neuroma were identified through a Medline search. Studies with at least 15 patients and a median follow-up longer than 12 months were included. The relationship between actuarial 5-year progression-free survival (PFS), and tumour and treatment parameters was examined. Forty-two studies were included. Tumour control following lower radiosurgery doses was similar to that reported following high doses. Only 12 studies reported actuarial outcomes at 5 years. There was no relationship between PFS at 5 years and dose to the tumour margin. Radiosurgery of larger tumours was associated with lower 5 year PFS (p < 0.05). Although on initial inspection radiosurgery of acoustic neuroma with doses of 12 - 13 Gy seems to be as effective as higher dose treatment, the available reports are subject to a number of confounding factors, are not sufficiently statistically powered and there is only limited long-term actuarial outcome data. Currently, available studies do not provide sufficient confidence to support the claim that low dose radiosurgery is equally effective as higher doses in the long-term control of acoustic neuroma.
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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".