The impact of prophylactic fresh-frozen plasma and cryoprecipitate on the incidence of central nervous system thrombosis and hemorrhage in children with acute lymphoblastic leukemia receiving asparaginase
Bibliographic record
Abstract
Asparaginase (ASP) therapy is associated with depletion of antithrombin (AT) and fibrinogen (FG). Potential toxicities include central nervous system thrombosis (CNST) and hemorrhage. Historical practice at the Izaak Walton Killam Health Centre (IWK) involves measuring AT and FG levels after ASP administration and transfusing fresh-frozen plasma (FFP) or cryoprecipitate (CRY) to prevent thrombotic and hemorrhagic complications. To determine whether this reduced these complications in children with acute lymphoblastic leukemia (ALL), incidence, outcome, and clinical characteristics of ASP-related CNST in ALL patients at IWK were compared with a similar cohort from BC Children's Hospital (BCCH), where prophylaxis was not performed. Costs associated with preventative versus expectant management were estimated. From 1990 to 2005, 240 patients were treated at IWK and 479 at BCCH. Seven BCCH patients developed venous CNST (1.5%), compared with none at IWK. CNST occurred exclusively during induction. Six patients received anticoagulation and continued ASP. All 7 patients remain in remission. National Cancer Institute high-risk ALL predicted CNST risk (P = .02), whereas sex, age, race, and body mass index did not. Neither FFP nor CRY protected against CNST, suggesting prophylaxis is unwarranted for unselected ALL patients. However, prophylactic replacement for HR patients in induction may be cost-effective.
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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 | 0.005 |
| 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 | 0.001 |
| 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".