Bibliographic record
Abstract
Breast cancer (BC) has the highest mortality rate among women’s cancers worldwide, and incidence rates are rising in low- and middle-income countries (LMIC). Screening programs are well-established in most high-income countries, but there is much debate about screening frequency and the optimal screening ages. In LMIC screening infrastructure is insufficient. Optical spectroscopy (OS) can be used as a pre-screening technique to measure breast composition and predict mammographic density (MBD), a known BC risk factor. The goal of this thesis was to develop and evaluate an OS device which is portable, low cost and requires minimal operator interaction. The device was based on a research prototype used in previous studies, with two major changes: (1) a 13-laser-wavelength module replaced the broadband light source and (2) the source and detector positions were fixed within rigid holders of different sizes. The wavelengths critical for distinguishing between BC risk groups were selected using a principal components analysis of data from previous studies. Source and detector positions were chosen to match the optically-interrogated volumes of the original device via Monte Carlo simulations of photon propagation. Two versions were developed – one for women (the Cups device) and one for girls (the LEGACY device). The Cups device was evaluated in comparison with the research prototype on its ability to predict MBD. For both devices, women with high MBD could be identified from spectra with high sensitivity and specificity and correlation between mammographic percent density (MPD) and OS-predicted MPD was significant, although slightly weaker for the Cups device (r = 0.62 vs. r = 0.74). For girls, OS had been used as an objective method for distinguishing between breast development stages. Spectral analysis using only the wavelengths from the LEGACY device showed that the reduced spectral content does not affect the ability to distinguish between development stages.
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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 | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".