Important Role of Routine Cerebrospinal Fluid Examination in Diagnosing Central Nervous System Relapse during Maintenance Therapy in Pediatric Acute Lymphoblastic Leukemia.
Bibliographic record
Abstract
Abstract Despite the success of central nervous system (CNS) preventive therapy in reducing the incidence of CNS recurrence in pediatric acute lymphoblastic leukemia (ALL) on therapy, CNS relapse continues to be a significant cause of treatment failure and is observed in 5–10% of patients. While most pediatric ALL treatment protocols mandate periodic lumbar puncture (LP) surveillance for the length of therapy, some centers have stopped collecting routine CSF samples unless there are suggestive signs or symptoms of CNS involvement. Our objective herein was to assess the value of routine cerebrospinal fluid (CSF) obtained while performing routine LPs for administration of intrathecal chemotherapy in diagnosing CNS relapse in children with ALL in the maintenance phase. Patients and Methods: All children (0–18 years) with ALL, diagnosed and treated at the Hospital for Sick Children, Toronto, Canada between 1994–2004 were subjected to a retrospective analysis. Original reports for CNS relapse during maintenance therapy were examined to determine whether CNS relapse was diagnosed based on routine CSF sample obtained while administering intrathecal chemotherpy or a CSF sample obtained based on signs and symptoms or after a diagnosis of a bone marrow relapse. Results: Four hundred and ninety four children were diagnosed and treated in our institution during the study period. Children were treated based on the children oncology group (COG), pediatric oncology group (POG) and local protocols where applicable. Thirty-one children (6.3%) suffered CNS relapse while on maintenance therapy. Twenty-one had an isolated CNS relapse and ten had a combined bone marrow and CNS relapse. Seventy-six percent (16/21) isolated CNS relapses were diagnosed base on routine CSF samples obtained from asymptomatic children while administering intratheacal chemotherapy. Conversely, all patients with combined bone marrow and CNS relapse presented with symptoms and signs that warranted CSF examination. Conclusion: Routine CSF examinations are important in detecting CNS relapse in children with ALL during maintenance therapy. Furthermore, routine CSF sampling may detect isolated CNS relapse in asymptomatic children with ALL prior to extension into combined relapse where prognosis is less favorable.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".