Continuous reference intervals for pediatric testosterone, sex hormone binding globulin and free testosterone using quantile regression
Bibliographic record
Abstract
Testosterone (T), sex hormone binding globulin (SHBG), free testosterone (FT) and bioavailable testosterone (BAT) are commonly employed tests in pediatric endocrinology and all require age-dependent reference intervals for interpretation. The common methods used to derive these reference intervals require decisions about data shape and/or age partition thresholds, which can result in sharp differences between age groups, particularly for pubescent children. Partitioning also results in a form of data loss, where data from one age-bin is completed disconnected from the adjacent age-bins. Non-parametric continuous reference intervals methods have previously been developed to avoid some of these drawbacks. These strategies use all the available data and smooth transitions between ages avoiding partitioning. However, the fitting process involves selection and adjustment of many parameters and it can be difficult to maintain a reproducible approach. Here we provide a workflow for non-parametric continuous reference intervals applied to T, FT, BAT, and SHBG using the R language quantregGrowth package. T measurements were determined by LC-MS/MS, FT and BAT were calculated, and SHBG was measured on the Roche Cobas e601. The continuous interval methodology is described in detail with code examples and illustrations for reproducibility.
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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 | 0.016 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".