Evaluation of different scan methods for the urinary detection of corticosteroid metabolites by liquid chromatography tandem mass spectrometry
Bibliographic record
Abstract
Different approaches for the non-target detection of corticosteroids in urine have been evaluated. As a result of previous studies about the ionization (positive/negative) and fragmentation of corticosteroids, several methods based on both precursor ion (PI) and neutral loss (NL) scans are proposed. The applicability of these methods was checked by the injection of a standard solution containing 19 model compounds. Five of the studied methods (NL of 76 Da; PI of 77, 91 and 105; PI of 237; PI of 121, 147 and 171; and NL of 38 Da) exhibited satisfactory results at the concentration level checked (corresponding to 20 ng/ml in sample). Some other methods in negative ionization mode such as the NL of 104 Da were found to lack sufficient sensitivity. Some of the applied methods were found to be specific for a concrete structure (NL of 38 Da for fluorine containing corticosteroids) while others showed a wide range applicability (PI of 77, 91 and 105 showed response in all model compounds). Interference by endogenous compounds was also tested by the analysis of negative urines and urines spiked with different corticosteroids. The suitability of these methods for the detection of corticosteroid metabolites was checked by the analysis of urine samples collected after the administration of methylprednisolone and triamcinolone. A combination of the reported methods seems to be the approach of choice in order to have a global overview about the excreted corticosteroid metabolites.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".