Improving the Accuracy of Biologically Effective Dose Estimates, from a Previously Published Study, After Radiosurgery for Acoustic Neuromas
Bibliographic record
Abstract
OBJECTIVE: To recalculate biological effective dose values (BED) for radio-surgical treatments of acoustic neuroma from a previous study. BEDs values were previously overestimated by only using beam-on times in calculations, so excluding the important beam-off-times (when deoxyribonucleic acid repair continues) which contribute to the overall treatment time. Simple BED estimations using a mono-exponential approximation may not always be appropriate but if used should include overall treatment time. METHODS: Time intervals between isocenters were estimated. These were especially important for the Gamma Knife Model 4C cases since manual changes significantly increase overall treatment times. Individual treatment parameters, such as iso-center number, beam-on-time, and beam-off-time, were then used to calculate BED values using a more appropriate bi-exponential model that includes fast and slow components of DNA damage repair over a wider time range. RESULTS: , indicated that revised values were consistently reduced when compared with originally quoted values, especially for 4C compared with Perfexion cases. Furthermore, subdivision of 4C cases by collimator number further emphasized the impact of scheduled gap times on BED. Further analysis demonstrated important limitations of the mono-exponential model. Target volume was a major confounding factor in the interpretation of the results of this study. CONCLUSIONS: BED values should be estimated by including beam-on and beam-off times. Suggestions are provided for more accurate BED estimations in future studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".