A multi‐wavelength, laser‐based optical spectroscopy device for breast density and breast cancer risk pre‐screening
Bibliographic record
Abstract
Optical Breast Spectroscopy (OBS) has been shown to predict mammographic breast density, a strong breast cancer risk factor. OBS is a low-cost technique applicable at any age. OBS information may be useful for personalizing breast cancer screening programs based on risk to improve consensus on and adherence to screening guidelines. To facilitate the use of OBS in population-wide studies, a research prototype OBS device was modified to make it portable and cheaper and to require less operator interaction. Two major changes were made: (1) the broadband light source was replaced with a laser module with 13 individual wavelengths turned on sequentially, enabling the use of photodiode detectors instead of a spectrometer, and (2) the light sources and detectors were placed in fixed positions within 4 sizes of cup, eliminating the need for placement by the operator. Wavelengths were selected using data from two previous studies. The reduction in spectral content did not significantly reduce the ability to distinguish between different risk groups. Positions for the light sources and detectors were chosen based on Monte Carlo simulations to match the optically interrogated volumes of the original device. Two light sources and six detectors per cup were used in the final design.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".