Cavity theory applied to the dosimetry of systemic radiotherapy of bone metastases
Bibliographic record
Abstract
A two-component model of an osteoblastic metastatic lesion has been developed to determine the absorbed dose delivered to soft tissue during systemic radiotherapy of osseous metastases. Doses to soft tissue from radioisotopes distributed in bone were calculated using Burlin's general cavity theory. A correction term was used to account for the absence of charged particle equilibrium within the metastatic lesion. Radiation doses for 153Sm, 186Re, 89Sr and 32P were calculated for several physiologically realistic lesion structures. Burlin's cavity weighting factor was greatest for higher energy isotopes and it decreased as the soft tissue cavity size increased. The correction for the absence of charged particle equilibrium also decreased with soft tissue pathlength, but increased with average bone pathlengths. Doses to soft tissue cavities ranged from 0.1 to 0.2 Gy MBq(-1) d(-1) for 153Sm to 0.5 to 0.6 Gy MBq(-1) d(-1) for 32P. Using the factors calculated in this work, the dose to soft tissue cavities within bone metastases can be calculated when the dose to adjacent bone has been determined, perhaps by autoradiography or electron paramagnetic resonance dosimetry. The doses calculated with this more accurate model of bone metastases demonstrate errors of 20% to 50% in previous calculations of the average dose to homogeneous metastatic lesions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".