The effect of skin thickness determined using breast CT on mammographic dosimetry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The effect of breast skin thickness on dosimetry in mammography was investigated. Breast computed tomography (CT) acquisition techniques, combined with algorithms designed for determining specific breast metrics, were useful for estimating skin thickness. A radial-geometry edge detection scheme was implemented on coronal reconstructed breast CT (bCT) images to measure the breast skin thickness. Skin thickness of bilateral bCT volume data from 49 women and unilateral bCT volume data from 2 women (10 healthy women and 41 women with BIRADS 4 and 5 diagnoses) was robustly measured with the edge detection scheme. The mean breast skin thickness (+/-inter-breast standard deviation) was found to be 1.45 +/- 0.30 mm. Since most current published normalized glandular dose (DgN) coefficients are based on the assumption of a 4-mm breast skin thickness, the DgN values computed with Monte Carlo techniques will increase up to 18% due to the thinner skin layers (e.g., 6-cm 50% glandular breast, 28 kVp Mo-Mo spectrum). The thinner skin dimensions found in this study suggest that the current DgN values used for mammographic dosimetry lead to a slight underestimate in glandular dose.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it