Poster - Thur Eve - 72: Clinical Subtleties of Flattening-Filter-Free Beams
Bibliographic record
Abstract
Flattening-filter-free (fff) beams offer superior dose rates, reducing treatment times for important techniques that utilize small field sizes, such as stereotactic ablative radiotherapy (SABR). The impact of ion collection efficiency (Pion) on the percent depth dose (PDD) has been discussed at length in the literature. Relative corrections of the order of l%–2% are possible. In the process of commissioning 6fff and 10fff beams, we identified a number of other important details that influence commissioning. We looked at the absolute dose difference between corrected and uncorrected PDD. We discovered a curve with a broad maximum between 10 and 20 cm. We wondered about the consequences of this PDD correction on the absolute dose calibration of the linac because the TG-51 protocol does not correct the PDD curve. The quality factor kQ depends on the PDD, so in principle, a correction to the PDD will alter the absolute calibration of the linac. Finally, there are other clinical tables, such as TMR, which are derived from PDD. Attention to details on how this computation is performed is important because different corrections are possible depending the method of calculation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.006 |
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".