Comparison of Clinical, Maternal, and Self Pubertal Assessments: Implications for Health Studies
Bibliographic record
Abstract
BACKGROUND: Most epidemiologic studies of puberty have only 1 source of pubertal development information (maternal, self or clinical). Interpretation of results across studies requires data on reliability and validity across sources. METHODS: The LEGACY Girls Study, a 5-site prospective study of girls aged 6 to 13 years (n = 1040) collected information on breast and pubic hair development from mothers (for all daughters) and daughters (if ≥10 years) according to Tanner stage (T1-5) drawings. At 2 LEGACY sites, girls (n = 282) were also examined in the clinic by trained professionals. We assessed agreement (κ) and validity (sensitivity and specificity) with the clinical assessment (gold standard) for both the mothers' and daughters' assessment in the subcohort of 282. In the entire cohort, we examined the agreement between mothers and daughters. RESULTS: Compared with clinical assessment, sensitivity of maternal assessment for breast development was 77.2 and specificity was 94.3. In girls aged ≥11 years, self-assessment had higher sensitivity and specificity than maternal report. Specificity for both mothers and self, but not sensitivity, was significantly lower for overweight girls. In the overall cohort, maternal and daughter agreement for breast development and pubic hair development (T2+ vs T1) were similar (0.66, [95% confidence interval 0.58-0.75] and 0.69 [95% confidence interval 0.61-0.77], respectively), but declined with age. Mothers were more likely to report a lower Tanner stage for both breast and pubic hair compared with self-assessments. CONCLUSIONS: These differences in validity should be considered in studies measuring pubertal changes longitudinally when they do not have access to clinical assessments.
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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 | 0.419 | 0.673 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".