Diagnostic Accuracy of Blood Lactate-to-Pyruvate Molar Ratio in the Differential Diagnosis of Congenital Lactic Acidosis
Bibliographic record
Abstract
BACKGROUND: Although the blood lactate-to-pyruvate (L:P) molar ratio is used to distinguish between pyruvate dehydrogenase deficiency (PDH-D) and other causes of congenital lactic acidosis (CLA), its diagnostic accuracy for differentiating between these 2 types of CLA has not been evaluated formally. METHODS: We conducted a retrospective study of all patients followed for mitochondrial diseases between 1985 and 2005 in a tertiary care pediatric hospital. RESULTS: At the recommended cut point of approximately 25, individual median L:P ratio demonstrated low sensitivity and specificity (77% and 91%, respectively) for differentiating between patients with enzymatically proven PDH-D (n = 11) and those with mitochondrial disease but normal pyruvate dehydrogenase (PDH) activity (non-PDH; n = 35). We observed a strong positive association between L:P ratio and blood lactate in non-PDH CLA, whereas this association was weak in PDH-D CLA. Consequently, patient classification based on median L:P ratio showed improved diagnostic accuracy at higher lactate concentrations: for lactate <2.5 mmol/L the area under the ROC curve was not statistically different from 0.5 (P = 0.3), whereas it was statistically different for lactate >2.5 mmol/L. In the 2.5 to 5.0 mmol/L lactate category, the sensitivity and specificity at an optimal cut point of 18.4 were 93% (95% CI, 77%-99%) and 71% (95% CI, 20%-96%), respectively; for lactate >5.0 mmol/L, with an optimal cut point of 25.8, sensitivity and specificity were 96% (95% CI, 77%-99%) and 100% (95% CI, 59%-100%), respectively. CONCLUSION: Usefulness of the L:P ratio for differentiating non-PDH and PDH-D types of CLA increases at higher lactate concentrations.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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".