Antithrombin and fibrinogen levels as predictors for plasma L‐asparaginase activity in children with acute lymphoblastic leukemia
Bibliographic record
Abstract
BACKGROUND: L-asparaginase is a cornerstone treatment for children with acute lymphoblastic leukemia (ALL). However, immune reaction to the drug may increase the clearance or impair the function of L-asparaginase and reduces its therapeutic efficacy. The objective of this study was to identify potential plasma proteins that could be used as proxies for L-asparaginase activity. METHODS: Fibrinogen, von Willebrand factor antigen (VWF:Ag), total protein, and albumin levels as well as antithrombin (AT) and L-asparaginase activities were measured in 97 children with ALL treated for prolonged period of time with L-asparaginase. Binary logistic regression and a receiver operating characteristic (ROC) curve analysis were performed to evaluate the predictive value of plasma proteins for L-asparaginase activity. RESULTS: Median E. coli L-asparaginase activity was 220 IU/L (range, 0-1308) throughout the treatment period. L-asparaginase activity was below 100 IU/L in 23% of measured samples. L-asparaginase activity was inversely associated with AT activity, fibrinogen, total protein, and albumin levels (r = -0.63, -0.62, -0.57, and -0.45, respectively; P < 0.0001), but not with VWF:Ag. ROC curve analyses showed an intermediate accuracy of AT activity (area under the ROC curve [AUC] = 0.77) to detect specimens with subtherapeutic level of L-asparaginase. An optimal accuracy was found when AT and fibrinogen were combined (AUC = 0.82; sensitivity = 75%; specificity = 82%; positive predictive value = 55%; negative predictive value = 92%) with cutoff values of 0.73 IU/mL and 1.85 g/L, respectively. CONCLUSIONS: AT combined with fibrinogen levels could be used as a proxy to identify patients with therapeutic level of L-asparaginase activity in the absence of real-time asparaginase measurement during prolonged exposure to L-asparaginase.
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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) | 0.000 | 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 | 0.000 | 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".