Multimodality Imaging for Vertebral Metastases in a Rat Osteolytic Model
Bibliographic record
Abstract
Imaging modalities facilitate the detection of early bony metastases. Few studies specifically address vertebral metastases in animal models for preclinical (early, asymptomatic) disease. We performed intracardiac injection of human breast cancer (MT-1) cells in 35 athymic nude rats. We evaluated potential temporal differences in appendicular versus axial metastases as detectable by longitudinal in vivo conventional radiography (ie, fine detail radiography and two-dimensional fluoroscopy). We compared bioluminescent reporter imaging with conventional radiographs in the detection of vertebral metastasis, and compared bioluminescent imaging with subsequent ex vivo microcomputed tomography analysis of osteolysis. The mean survival was 25 days in the animals that had metastases develop. Conventional radiographs identified appendicular osteolysis by 14 days; however, vertebral osteolysis was identified late in the metastatic spread (Days 25-28). Bioluminescence imaging was more sensitive in earlier detection of vertebral lesions in all imaged animals at Day 21, which corresponded to microcomputed tomography evaluation of osteolysis. Conventional radiographs do not appear useful for early detection of vertebral metastasis. Early identification of metastasis is important when considering the use of this model to evaluate therapeutic outcomes directed toward vertebral metastasis.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".