Secondary neoplasms subsequent to Berlin-Frankfurt-Munster therapy of acute lymphoblastic leukemia in childhood: significantly lower risk without cranial radiotherapy
Bibliographic record
Abstract
Secondary neoplasms (SNs) represent serious late complications after successful treatment of malignant diseases. To evaluate the rate and type of SNs after Berlin-Frankfurt-Münster (BFM) treatment in children with acute lymphoblastic leukemia (ALL), we analyzed the data from the BFM database and the German Childhood Cancer Registry (GCCR). Between April 1979 and April 1995, 5006 children with B-precursor or T-ALL were enrolled in 5 ALL-BFM multicenter trials. The median follow-up time from diagnosis was 5.7 years (range 1.5-18 years). By December 1997, 52 SNs were documented, including 16 acute myeloid leukemias (AMLs), 13 neoplasms of the central nervous system (CNS), and 23 other neoplasms. Compared with the expected numbers estimated from incidence rates derived from the GCCR, this represented a 14-fold increase for all cancers and a 19-fold increase for CNS tumors. SNs developed 0.9 to 15 years (median: 6 years) after the diagnosis of ALL; 46 patients were in first complete remission (CR). The overall cumulative risk of SNs at 15 years was 3.3% (95% confidence interval [CI]: 1.6%-5.1%) and 2.9% (95% CI: 1.6%-4.2%) in first CR. The risk was 3.5% (95% CI: 1.5%-5. 5%) after treatment, including cranial irradiation and significantly lower in nonirradiated patients: 1.2% (95% CI: 0.2%-2.3%; P =.048). The development of secondary AML was not associated with the use of any specific cytotoxic agent. Considering the high-survival rate of this large unselected ALL cohort, the risk of SN is relatively low, though higher, especially after cranial irradiation, than in the general population. Long-term follow-up is mandatory, and further SNs with longer latency periods are to be expected. (Blood. 2000;95:2770-2775)
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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 | 0.001 | 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 | 0.000 |
| 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".