Comparison of methods to assess onset of breast development in the LEGACY Girls Study: methodological considerations for studies of breast cancer
Bibliographic record
Abstract
BACKGROUND: Younger age at onset of breast development, which has been declining in recent decades, is associated with increased breast cancer risk independent of age at menarche. Given the need to study the drivers of these trends, it is essential to validate methods to assess breast onset that can be used in large-scale studies when direct clinical assessment of breast onset is not feasible. METHODS: Breast development is usually measured by Tanner stages (TSs), assessed either by physical examination or by mother's report using a picture-based Sexual Maturation Scale (SMS). As an alternative, a mother-reported Pubertal Development Scale (PDS) without pictures has been used in some studies. We compared agreement of SMS and PDS with each other (n = 1022) and the accuracy of PDS with clinical TS as a gold standard for the subset of girls with this measure (n = 282) using the LEGACY cohort. We further compared prediction of breast onset using ROC curves and tested whether adding urinary estrone 1-glucuronide (E1G) improved the AUC. RESULTS: The agreement of PDS with SMS was high (kappa = 0.80). The sensitivity of PDS vs clinical TS was 86.6%. The AUCs for PDS alone and SMS alone were 0.88 and 0.79, respectively. Including E1G concentrations improved the AUC for both methods (0.91 and 0.86 for PDS and SMS, respectively). CONCLUSIONS: The PDS without pictures is a highly accurate, sensitive, and specific method for assessing breast onset, especially in settings where clinical TS is not feasible. In addition, it is comparable to SMS methods with pictures and thus easier to implement in large-scale studies, particularly phone-based interviews where pictures may not be available. Urinary E1G can improve accuracy over than PDS or SMS alone.
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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 | 0.460 | 0.471 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".