A multi‐wavelength, laser‐based optical spectroscopy device for breast density and breast cancer risk pre‐screening
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
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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