Conservative Management Versus Stereotactic Radiation for Vestibular Schwannomas
Bibliographic record
Abstract
OBJECTIVE: To compare the long-term outcome of hearing and tumor control of small vestibular schwannomas treated with conservative management or radiation therapy. DATA SOURCES: A thorough search for English-language publications and "in-process" articles dating from 1948 to January 2011 was performed using Ovid MEDLINE. STUDY SELECTION: The principal criteria were patients with no microsurgical intervention before their treatment, a follow-up of at least 5 years, and a useful hearing level at diagnosis. DATA EXTRACTION: Fourteen studies met our criteria. Hearing preservation outcome (worse, unchanged, and better) and tumor size outcome (growth, unchanged, and regression) data were collected from the articles, as well as all other significant observations. No studies using fractionated stereotactic radiotherapy met our criteria. Stereotactic radiation was the only radiation therapy analyzed. DATA SYNTHESIS: The data were analyzed using the Pearson χ2 test. CONCLUSION: Current data on conservative management and stereotactic radiation do not provide enough evidence to make a clear conclusion on the outcome of useful hearing preservation in a long-term manner. In this study, however, stereotactic radiation is shown to have better tumor control rates than conservative management. Additional studies reporting long-term follow-ups of patients are required so as to provide the medical field with a better understanding of vestibular schwannoma treatment therapies.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".