Qualitative urinary organic acid analysis: Methodological approaches and performance
Bibliographic record
Abstract
A programme for proficiency testing of biochemical genetics laboratories undertaking urinary qualitative organic acid analysis and its results for 50 samples examined for factors contributing to poor performance are described. Urine samples from patients in whom inherited metabolic disorders have been confirmed as well as control urines were circulated to participants and the results from 94 laboratories were evaluated. Laboratories showed variability both in terms of their individual performance and on a disease-specific basis. In general, conditions including methylmalonic aciduria, propionic aciduria, isovaleric aciduria, mevalonic aciduria, Canavan disease and 3-methylcrotonyl-CoA carboxylase were readily identified. Detection was poorer for other diseases such as glutaric aciduria type II, glyceric aciduria and, in one sample, 3-methylcrotonyl-CoA carboxylase deficiency. To identify the factors that allow some laboratories to perform well on a consistent basis while others perform badly, we devised a questionnaire and compared the responses with the results for performance in the scheme. A trend towards better performance could be demonstrated for those laboratories that regularly use internal quality control (QC) samples in their sample preparation (p = 0.079) and those that participate in further external quality assurance (EQA) schemes (p = 0,040). Clinicians who depend upon these diagnostic services to identify patients with these defects and the laboratories that provide them should be aware of the potential for missed diagnoses and the factors that may lead to improved performance.
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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 | 0.035 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".