Inter-comparison of relative stopping power estimation models for proton therapy
Bibliographic record
Abstract
Theoretical stopping power values were inter-compared for the Bichsel, Janni, ICRU and Schneider relative stopping power (RSP) estimation models, for a variety of tissues and tissue substitute materials taken from the literature. The RSPs of eleven plastic tissue substitutes were measured using Bragg peak shift measurements in water in order to establish a gold standard of RSP values specific to our centre's proton beam characteristics. The theoretical tissue substitute RSP values were computed based on literature compositions to assess the four different computation approaches. The Bichsel/Janni/ICRU approaches led to mean errors in the RSP of -0.1/+0.7/-0.8%, respectively. Errors when using the Schneider approach, with I-values from the Bichsel, Janni and ICRU sources, followed the same pattern but were generally larger. Following this, the mean elemental ionisation energies were optimized until the differences between theoretical RSP values matched measurements. Failing to use optimized I-values when applying the Schneider technique to 72 human tissues could introduce errors in the RSP of up to -1.7/+1.1/-0.4% when using Bichsel/Janni/ICRU I-values, respectively. As such, it may be necessary to introduce an additional step in the current stoichiometric calibration procedure in which tissue insert RSPs are measured in a proton beam. Elemental I-values can then optimized to match these measurements, reducing the uncertainty when calculating human tissue RSPs.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".