Temporal compartmental dosing effects for robotic prostate stereotactic body radiotherapy
Bibliographic record
Abstract
The rate of dose accumulation within a given area of a target volume tends to vary significantly for non-isocentric delivery systems such as Cyberknife stereotactic body radiotherapy. In this study, we investigated whether intra-target temporal dose distributions produce significant variations in the biological equivalent dose. For the study, time courses of ten patients were reconstructed and calculation of a biologically equivalent uniform dose (EUD) was performed using a formula derived from the linear quadratic model (α/β = 3 for prostate cancer cells). The calculated EUD values obtained for the actual patient treatments were then compared with theoretical EUD values for delivering the same physical dose distribution except that the whole target being irradiated continuously (e.g. large-field 'dose-bathing' type of delivery). For all the case, the EUDs for the actual treatment delivery were found to correlate strongly with the EUDs for the large-field delivery: a linear correlation coefficient of R² = 0.98 was obtained and the average EUD for the actual Cyberknife delivery was somewhat higher (5.0 ± 4.7%) than that for the large-field delivery. However, no statistical significance was detected between the two types of delivery (p = 0.21). We concluded that non-isocentric small-field Cyberknife delivery produced consistent biological dosing that tracked well with the constant-dose-rate, large-field-type delivery for prostate stereotactic body radiotherapy.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".