Estimation of mammographic density on an interval scale by transillumination breast spectroscopy
Bibliographic record
Abstract
Transillumination breast spectroscopy (TiBS) uses nonionizing optical radiation to gain information about breast tissue morphological and structural properties. TiBS spectra are obtained from 232 women and compared to mammographic density (MD) quantified using Cumulus. The ability of TiBS to estimate MD is assessed using partial least-squares (PLS) regression methods, which requires TiBS spectra as input (X) and Cumulus MD as target (Y) data. Multiple PLS models are considered to determine the optimal processing technique(s) for the input (X) and target (Y) data. For each model, the association between TiBS estimated MD (Y) and Cumulus MD (Y) is established using Spearman's rank correlation and linear regression analysis. The model that best estimates MD has the fewest assumptions regarding target (Y) and spectral (X) processing. The Spearman's correlation coefficient between predicted MD and Cumulus MD for this model is 0.88, with a regression slope (beta) of 0.93 (95% CI 0.83-1.02) and an R(2) of 0.78. The approximation of individual MD was within 10% of Cumulus MD for the majority of women (80%), without stratification on age, body mass index (BMI), and menopausal status. TiBS provides an alternative to mammography assessed MD enabling frequent and earlier use of MD as a risk marker in preventive oncology.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".