Factors influencing the steroid profile in doping control analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Steroid profiling is one of the most versatile and informative screening tools for the detection of steroid abuse in sports drug testing. Concentrations and ratios of various endogenously produced steroidal hormones, their precursors and metabolites including testosterone (T), epitestosterone (E), dihydrotestosterone (DHT), androsterone (And), etiocholanolone (Etio), dehydroepiandrosterone (DHEA), 5alpha-androstane-3alpha,17beta-diol (Adiol), and 5beta-androstane-3alpha,17beta-diol (Bdiol) as well as androstenedione, 6alpha-OH-androstenedione, 5beta-androstane-3alpha,17alpha-diol (17-epi-Bdiol), 5alpha-androstane-3alpha,17alpha-diol (17-epi-Adiol), 3alpha,5-cyclo-5alpha-androstan-6beta-ol-17-one (3alpha,5-cyclo), 5alpha-androstanedione (Adion), and 5beta-androstanedione (Bdion) add up to a steroid profile that is highly sensitive to applications of endogenous as well as synthetic anabolic steroids, masking agents, and bacterial activity. Hence, the knowledge of factors that do influence the steroid profile pattern is a central aspect, and pharmaceutical (application of endogenous steroids and various pharmaceutical preparations), technical (hydrolysis, derivatization, matrix), and biological (bacterial activities, enzyme side activities) issues are reviewed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it