Primary Hyperoxaluria Screening and Monitoring: Quantitative Measurement of Plasma Oxalate by Gas Chromatography-Mass Spectrometry With High Sensitivity
Bibliographic record
Abstract
Background: Plasma oxalate measurements can be used for the screening and therapeutic monitoring of primary hyperoxaluria. We developed a gas chromatography-mass spectrometry (GC-MS) assay for plasma oxalate measurements with high sensitivity and suitable testing volumes for pediatric populations. Methods: -oxalate ion (m/z: 263.15) as the internal standard. Method validation included determination of the linear range, limit of blank, limit of detection, lower limit of quantification, precision, recovery, carryover, interference, and dilution effect. The cut-off value between primary and non-primary hyperoxaluria in a pediatric population was analyzed. Results: The detection limit was 0.78 μmol/L, and the linear range was up to 80.0 μmol/L. The between-day precision was 5.7% at 41.3 μmol/L and 13.1% at 1.6 μmol/L. The carryover was <0.2%. The recovery rate ranged from 90% to 110%. Interference analysis showed that Hb did not interfere with plasma oxalate quantification, whereas intralipids and bilirubin caused false elevation of oxalate concentrations. A cut-off of 13.9 μmol/L showed 63% specificity and 77% sensitivity, whereas a cut-off of 4.15 μmol/L showed 100% specificity and 20% sensitivity. The minimum required sample volume was 250 μL. The detected oxalate concentrations showed interference from instrument conditioning, sample preparation procedures, medications, and various clinical conditions. Conclusions: GC-MS is a sensitive assay for quantifying plasma oxalate and is suitable for pediatric patients. Plasma oxalate concentrations should be interpreted in a clinical context.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".