Quantification of 2-methylcitric acid in dried blood spots improves newborn screening for propionic and methylmalonic acidemias
Bibliographic record
Abstract
Background Newborn screening for propionic acidemia and methylmalonic acidurias using the marker propionylcarnitine (C3) is neither sensitive nor specific. Using C3 to acetylcarnitine (C3/C2) ratio, together with conservative C3 cut-offs, can improve screening sensitivity, but the false positive rate remains high. Incorporating the marker 2-methylcitric acid has been suggested, to improve the positive predictive value for these disorders without compromising the sensitivity. Methods Between July 2011 and December 2012 at the Newborn Screening Ontario laboratory, all neonatal dried blood spot samples that were reported as screen positive for propionic acidemia or methylmalonic acidurias based on elevated C3 and C3/C2 ratio were analyzed for 2-methylcitric acid, using liquid chromatography tandem mass spectrometry. Results Of 222,420 samples screened, 103 were positive for methylmalonic acidurias or propionic acidemia using C3 and C3/C2 ratio as markers. There were nine true positives: propionic acidemia (n = 3), Cobalamin (Cbl) A (n=1), and Cbl C (n = 5). Among false positives there were 72 neonates not affected, 20 with maternal B 12 deficiency, and two incidental finding (transcobalamin II and unclassified Cbl defect). 2-Methylcitric acid was analyzed in all 103 samples and ranged between 0.1 and 89.4 µmol/l (reference range 0.04–0.36). Only 14 samples exceeded the set 2-methylcitric acid cut-off of 1.0 µmol/l, including the samples from all nine true positives. Conclusion By including 2-methylcitric acid in the screening algorithm, the positive predictive value of our primary and secondary screening targets improved from 8.7 to 64.3%. This would have eliminated 89 unnecessary referrals while maintaining 100% sensitivity.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".