Detection of central nervous system involvement in childhood acute lymphoblastic leukemia by cytomorphology and flow cytometry of the cerebrospinal fluid
Bibliographic record
Abstract
BACKGROUND: Therapy directed at the central nervous system (CNS) is an essential part of the treatment for childhood acute lymphoblastic leukemia (ALL). The current evaluation of CNS involvement based on cytomorphological examination of the cerebrospinal fluid (CSF) alone is not as sensitive with low cell counts as flow cytometric immunophenotyping (FCI) of the CSF. However, the importance of low CSF blasts counts at diagnosis is uncertain. We sought to determine the significance of FCI in relation to conventional morphological examination. PROCEDURE: We retrospectively compared FCI of the CSF with cytomorphology at diagnosis or relapse of childhood ALL. All patients were diagnosed 2000-2012 in Stockholm or Umeå, Sweden. Clinical data were collected from medical records and the Nordic leukemia registry. Treatment assignment was based on morphological examination only. RESULTS: The cohort was comprised of 214 patients with ALL. CSF involvement was detected by both methods in 20 patients, in 17 by FCI alone, and in one patient by cytomorphology alone. The relapse rate was higher for patients with negative cytology but positive FCI compared to those without CNS involvement using both methods. The difference was especially marked in the current protocol. However, none of the patients with negative CSF cytology but positive FCI had a CNS relapse. CONCLUSIONS: FCI of the CSF increased the detection rate of CNS involvement of ALL approximately two times compared to cytomorphology. Patients with low-level CNS involvement may benefit from additional intensified systemic or CNS-directed therapy, but larger studies are needed.
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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 | 0.002 | 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 | 0.000 |
| 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".