<scp>L</scp>‐Asparaginase lowers plasma antithrombin and mannan‐binding‐lectin levels: Impact on thrombotic and infectious events in children with acute lymphoblastic leukemia
Bibliographic record
Abstract
BACKGROUND: L-asparaginase, a key therapeutic agent in the management of patients with acute lymphoblastic leukemia (ALL), dramatically impairs hepatic protein synthesis. We investigated the effects of prolonged exposure to L-asparaginase on antithrombin (AT), fibrinogen and mannan-binding-lectin (MBL) levels, and on the occurrence of thrombotic events (TE) and febrile neutropenia episodes (FN) in pediatric patients. PROCEDURE: Protein levels were measured in 97 children during 30 weeks of chemotherapy with L-asparaginase and up to 1 year following remission. TE and FN episodes were recorded during this period. RESULTS: Median AT level decreased from 0.96 IU/mL prior to treatment (range: 0.69-1.38) to 0.55 IU/mL (0.37-0.76) during therapy. Fibrinogen and MBL decreased from 3.18 g/L (1.29-7.28) and 1,177 ng/mL (57-5,343) to 1.56 g/L (0.84-2.13) and 193 ng/mL (57-544), respectively. All three proteins had recovered 1-4 weeks after L-asparaginase cessation. TE were reported in 22 (23%) patients. Of these, 11 occurred after a median of 10 administrations of L-asparaginase. Fifty-one FN were associated with infections, of which 36 occurred during treatment with L-asparaginase. Patients with low levels of MBL at diagnosis were at higher risk of FN associated with infections (RR = 1.59, 95%CI: 1.026-2.474). Both AT and MBL decreases were moderately correlated with fibrinogen (r = 0.51 and 0.58, respectively). CONCLUSIONS: Children with ALL are exposed to significant decrease in AT, fibrinogen and MBL levels, and concomitant increased risk of thrombosis and FN with infection during L-asparaginase treatment. Measuring plasma levels of these liver-derived proteins could help predict the occurrence of adverse events.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".