Overcoming the challenge of confident identification among two related groups of 17-methyl steroids by GC-MS
Bibliographic record
Abstract
Tetrahydromethyltestosterone (THMT) and 20 hydroxymethyl 18-nortetrahydromethyltestosterone (20OHnorTHMT) are metabolites of the anabolic androgenic steroids methyltestosterone and metandienone. Both molecule structures are used as markers in anti doping analysis. There are eight reasonable diastereomeric structures of each group relevant for metabolic purposes. Despite a different chemical composition, mass spectrometry reaches its limitation in the discrimination of these two groups in its entirety. Due to the low abundance of the molecular ion, high resolution mass spectrometry provides shared fragment ions challenging identification by extracted ion chromatograms out of full scan mode acquisitions. Further on, tandem mass spectrometry uses partly the same ion transitions for both groups of targeted analytes. Thus, confident identification of signals needs chromatography as an additional dimension. Therefore, a gas chromatographic method using a DB-5ms capillary column (30 m, 0.25 mm, 0.25 µm) was developed. Hence, discrimination between the two groups was enabled and a confident structural assignment between the eight diastereomers was achieved. This case study contributes to a higher quality of anti doping analysis, but even further raises awareness of the importance of chromatographic separation in cases of insufficient mass spectrometric discrimination.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".