Relative toxicity of<sup>45</sup>Ca β-particles and<sup>242</sup>Cm α-particles following their intravenous injection into mice as radiolabelled FAP
Bibliographic record
Abstract
PURPOSE: To determine the relative toxicity of alpha- and beta-radiations under conditions of controlled temporal and spatial dose distribution. METHODS: Fused aluminosilicate particles were radiolabelled with either (45)Ca (a beta-emitter) or (242)Cm (an alpha-emitter). These were injected into CBA/Ca mice to give lifespan, whole-body doses of approximately 0.5, 1.0 or 1.5 Gy. Most animals were entered into a lifespan toxicity study, but some were killed for radiochemical analysis and autoradiography. RESULTS: Twenty-seven tumour types were identified. The most common malignant tumours were: Mammary carcinoma; liver carcinoma; malignant lymphoma; uterine histiocytic sarcoma. Excess relative risk (strictly hazard ratio) was higher for radiation-induced carcinomas than for sarcomas. The carcinomas, but not sarcomas showed a reduction in relative risk at the highest radiation dose employed. This reduction was most easily attributed to a systemic effect. The highest relative toxicity measured was for liver carcinoma (5.9, 95% confidence intervals [CI] 2.4, 14) and the lowest for uterine carcinoma (0.6, CI 0.03, 9.7). Overall, the excess relative risk ratio for SURVIVAL WAS 1.9 (CI 1.1, 3.2), FOR ALL CARCINOMA WAS 2.3 (CI 1.7, 3.0) AND FOR ALL SARCOMA WAS 2.7 (CI 0.72, 10). CONCLUSIONS: The 10-fold variability in the observed toxicity ratio for different tumour endpoints shows that tissue sensitivity is a more important determinant of relative toxicity than radiation quality. The use of single radiation-weighting (w(R)) factors for radiation risk prediction and for radiological protection dosimetry is inconsistent with scientific observation.
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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 | 0.001 |
| 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) | 0.001 | 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".