Non-invasive optical spectroscopic monitoring of breast development during puberty
Bibliographic record
Abstract
BACKGROUND: Tanner staging (TS), a five-stage classification indicating no breast tissue (TS1) to full breast development (TS5), is used both in health research and clinical care to assess the onset of breast development (TS2) and duration in each stage. Currently, TS is measured both visually and through palpation but non-invasive methods will improve comparisons across settings. METHODS: We used optical spectroscopy (OS) measures from 102 girls at the Ontario site of the LEGACY girls study (average age 12 years, range 10.0-15.4 years) to determine whether breast tissue optical properties map to each TS. We further examined whether these properties differed by age, body mass index (BMI), and breast cancer risk score (BCRS) by examining the major principal components (PC). RESULTS: Age and BMI increased linearly with increasing TS. Eight PCs explained 99.9% of the variation in OS data. Unlike the linear increase with age and BMI, OS components had distinct patterns by TS: the onset of breast development (TS1 to TS2) was marked by elevation of PC3 scores indicating an increase in adipose tissue and decrease in signal from the pectoral muscle; transition to TS3 was marked by elevation of PC6 and PC7 and decline of PC2 scores indicating an increase in glandular or dense tissue; and transition to TS4+ by decline of PC2 scores representing a further increase in glandular tissue relative to adipose tissue. Of the eight PCs, three component scores (PC4, PC5, and PC8) remained in the best-fitting model of BCRS, suggesting different levels of collagen in the breast tissue by BCRS. CONCLUSIONS: Our results suggest that serial measures of OS, a non-invasive assessment of breast tissue characteristics, can be used as an objective outcome that does not rely on visual inspection or palpation, for studying drivers of breast development.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".