TH‐C‐BRC‐07: An Investigation of the Effect of Micro‐CT Radiation Dose On Tumor Growth in Mice
Bibliographic record
Abstract
Purpose: This study is designed to determine the impact of longitudinal micro‐CT imaging on tumor growth in mice. Method and Materials: C57BL/6 mice were divided into four groups, a control group and 3 imaging groups. The control group received no imaging while each remaining group underwent a different imaging protocol. Each mouse received an inoculation of 2×10̂5 B16F1 cells (murine melanoma), subcutaneously in the right hind flank. All imaging was performed on a fast volumetric micro‐CT scanner (GE LocusUltra, London, Canada). The three imaging protocols were as follows: 1. ‘Low dose’ ‐ An 8 second scan with tube settings of 80 kVp and 70 mA (∼ 7 cGy entrance dose per scan), 2. ‘Medium dose’ — 30 second scan at 80 kVp and 50 mA (∼ 17 cGy entrance dose per scan) and 3. ‘High dose’ ‐ 50 second scan at 80 kVp and 50 mA (∼27 cGy entrance dose per scan). The imaging was performed four times: once every four days, starting on the fourth day post inoculation. After the final imaging session each tumor was excised, weighed and imaged (to obtain the final tumor volume) and then processed for histology. Final tumor mass and volume are used to evaluated the impact of longitudinal micro‐CT imaging on the tumor growth. Results: Preliminary results have been obtained for n=8 mice per group. An ANOVA test indicates a significant difference in mean final tumor mass (p = 0.049), with the ‘Low dose’ tumors being largest on average. Conclusion: These preliminary results indicate the ‘Low dose’ tumors have a faster growth rate than the tumors in the other groups, however the power of this test is low (β = 0.49). We are currently working to increase the sample size to n = 16, which should yield acceptable power (β ≈ 0.1).
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".