Sensitivity of GC‐EI/MS, GC‐EI/MS/MS, LC‐ESI/MS/MS, LC‐Ag<sup>+</sup>CIS/MS/MS, and GC‐ESI/MS/MS for analysis of anabolic steroids in doping control
Bibliographic record
Abstract
This study compared the sensitivity of various separation and ionization methods, including gas chromatography with an electron ionization source (GC-EI), liquid chromatography with an electrospray ionization source (LC-ESI), and liquid chromatography with a silver ion coordination ion spray source (LC-Ag(+) CIS), coupled to a mass spectrometer (MS) for steroid analysis. Chromatographic conditions, mass spectrometric transitions, and ion source parameters were optimized. The majority of steroids in GC-EI/MS/MS and LC-Ag(+) CIS/MS/MS analysis showed higher sensitivities than those obtained with other analytical methods. The limits of detection (LODs) of 65 steroids by GC-EI/MS/MS, 68 steroids by LC-Ag(+) CIS/MS/MS, 56 steroids by GC-EI/MS, 54 steroids by LC-ESI/MS/MS, and 27 steroids by GC-ESI/MS/MS were below cut-off value of 2.0 ng/mL. LODs of steroids that formed protonated ions in LC-ESI/MS/MS analysis were all lower than the cut-off value. Several steroids such as unconjugated C3-hydroxyl with C17-hydroxyl structure showed higher sensitivities in GC-EI/MS/MS analysis relative to those obtained using the LC-based methods. The steroids containing 4, 9, 11-triene structures showed relatively poor sensitivities in GC-EI/MS and GC-ESI/MS/MS analysis. The results of this study provide information that may be useful for selecting suitable analytical methods for confirmatory analysis of steroids.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".