Plasma oxalate: comparison of methodologies
Bibliographic record
Abstract
Measurement of oxalate in the blood is essential for monitoring primary hyperoxaluria patients with progressive renal impairment and on dialysis prior to transplantation. As no external quality assurance scheme is available for this analyte, we conducted a sample exchange scheme between six laboratories specifically involved with the investigation of primary hyperoxaluria to compare results. The methodologies compared were gas chromatography/mass spectrometry (GCMS), ion chromatography with mass spectrometry (ICMS), and enzymatic methods using oxalate oxidase and spectrophotometry. Although individual laboratories performed well in terms of reproducibility and linearity, there was poor agreement (absolute values) between centres as illustrated by a longer-term comparison of patient results from two of the participating laboratories. This situation was only partly related to differences in calibration and mainly reflected the lower recoveries seen with the ultrafiltration of samples. These findings lead us to conclude that longitudinal monitoring of primary hyperoxaluria patients with deteriorating kidney function should be performed by a single consistent laboratory and the methodology used should always be defined. In addition, plasma oxalate concentrations reported in registry studies and those associated with the risk of systemic oxalosis in published studies need to be interpreted in light of the methodology used. A reference method and external quality assurance scheme for plasma oxalate analysis would be beneficial.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".