Re-assessment of chronic radio-induced tissue damage in a rat hindlimb model
Bibliographic record
Abstract
Radiotherapy is successfully used to treat neoplastic lesions, but may adversely affect normal tissues within the irradiated volume. However, additional clinical and para-clinical data are required for a comprehensive understanding of the pathogenesis of this damage. We assessed a rat model using clinical records and medical imaging to gain a better understanding of irradiation-induced tissue damage. The hindlimbs of the rats in this model were irradiated with a single dose of 30 or 50 Gy. Sequential analysis was based on observation records of stage and planar scintigraphy. Additional radiography, radiohistology and histology studies were performed to detect histological alterations. All animals developed acute and late effects, with an increased severity after a dose of 50 Gy. The bone uptake of (99m)Tc-HDP was significantly decreased in a dose- and time-dependent manner. Histologically, significant tissue damage was observed. After the 50 Gy irradiation, the animals developed lesions characteristic of osteoradionecrosis (ORN). Radiographic and histological studies provided evidence of lytic bone lesions. Our rat model developed tissue damage characteristic of radiation injury after a single 30 Gy irradiation and tissue degeneration similar to that which occurs during human ORN after a 50 Gy irradiation. The development of this animal model is an essential step in exploring the pathogenesis of irradiation-induced tissue damage, and may be used to test the efficacy of new treatments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".