Quantitative ultrasound analysis for rib bones
Bibliographic record
Abstract
Breast cancer is one of the most common cancers among women. Radiotherapy can cause damage to rib bones, increasing the risk of rib bone fractures. By improving the accessibility of rib monitoring, treatment plans could be adjusted to reduce these side effects. Quantitative ultrasound analysis, which has proven effective in detecting osteoporosis, is being explored as a potential tool for assessing rib bone health. The study consists of two parts: first, a simulation model of rib alterations was developed to train a regression model for predicting bone volume fraction (BV/TV) using ultrasound backscatter parameters. In the second part, small-scale in-vivo measurements were performed on healthy volunteers to compare the backscatter parameters from the in-silico and in-vivo analyses. The model demonstrated a statistically significant correlation between BV/TV and backscatter analysis, with similar distributions observed between the in-silico and in-vivo backscatter parameters. However, the regression model's ability to predict BV/TV was less accurate for rib bones with the highest degree of erosion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".