Application of multisegment injection on quantification of creatinine and standard addition analysis of urinary 5‐hydroxyindoleacetic acid simultaneously with creatinine normalization
Bibliographic record
Abstract
Abstract In this paper, the development of a simple dilute‐and‐shoot method for quantifying urinary creatinine by CE–ESI–MS was described. The creatinine analysis time was about 7 min/sample by conventional single injection (SI) method and can be significantly reduced to less than 2 min/sample with multi‐segment injection (MSI). In addition, the standard addition analysis of 5‐hydroxyindole‐3‐acetic acid (5‐HIAA) and creatinine normalization was performed within one run by the MSI technique, and the total analysis time was 14‐min faster compared to the SI method for analyzing the same set of samples. The uses of isotopic and non‐isotopic internal standards (ISs) were compared. Creatinine‐(methyl‐13C) and 5‐hydroxyindole‐4,6,7‐D3‐3‐acetic‐D2 acid (5‐HIAA‐D5) used as isotopic ISs can provide both accurate and precise results. In contrast, 1,5,5‐trimethylhydantoin (1,5,5‐TH) used as the non‐isotopic IS for creatinine may cause a bias of over 13% in SI method and even worse when the MSI technique was used. Another compound, 2‐methyl‐3‐indoleacetic acid (2‐MIAA), was determined not suitable for MSI analysis of 5‐HIAA due to endogenous interferences despite its acceptable performance in conventional methods of analysis.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".