Dose to medium versus dose to water as an estimator of dose to sensitive skeletal tissue
Bibliographic record
Abstract
The purpose of this study is to determine whether dose to medium, D(m), or dose to water, D(w), provides a better estimate of the dose to the radiosensitive red bone marrow (RBM) and bone surface cells (BSC) in spongiosa, or cancellous bone. This is addressed in the larger context of the ongoing debate over whether D(m) or D(w) should be specified in Monte Carlo calculated radiotherapy treatment plans. The study uses voxelized, virtual human phantoms, FAX06/MAX06 (female/male), incorporated into an EGSnrc Monte Carlo code to perform Monte Carlo dose calculations during simulated irradiation by a 6 MV photon beam from an Elekta SL25 accelerator. Head and neck, chest and pelvis irradiations are studied. FAX06/MAX06 include precise modelling of spongiosa based on microCT images, allowing dose to RBM and BSC to be resolved from the dose to bone. Modifications to the FAX06/MAX06 user codes are required to score D(w) and D(m) in spongiosa. Dose uncertainties of approximately 1% (BSC, RBM) or approximately 0.5% (D(m), D(w)) are obtained after up to 5 days of simulations on 88 CPUs. Clinically significant differences (>5%) between D(m) and D(w) are found only in cranial spongiosa, where the volume fraction of trabecular bone (TBVF) is high (55%). However, for spongiosa locations where there is any significant difference between D(m) and D(w), comparisons of differential dose volume histograms (DVHs) and average doses show that D(w) provides a better overall estimate of dose to RBM and BSC. For example, in cranial spongiosa the average D(m) underestimates the average dose to sensitive tissue by at least 5%, while average D(w) is within approximately 1% of the average dose to sensitive tissue. Thus, it is better to specify D(w) than D(m) in Monte Carlo treatment plans, since D(w) provides a better estimate of dose to sensitive tissue in bone, the only location where the difference is likely to be clinically significant.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".