Chronic health conditions (CHC) and late mortality in survivors of acute lymphoblastic leukemia (ALL) in the Childhood Cancer Survivor Study.
Bibliographic record
Abstract
10016 Background: The impact of evolving risk-stratified therapy on long-term morbidity and mortality in survivors of childhood ALL remains largely unknown. Methods: All-cause and health-related late mortality (HRM; captures death from late-effects occurring > 5 yrs from diagnosis), subsequent (malignant) neoplasm [S(M)N], CTCAE graded CHC and neurocognitive outcomes were assessed in 5-yr survivors of ALL diagnosed < 21 yrs of age from 1970-99. Therapy combinations defined 6 groups: 1970s-like ( 70s), standard and high risk 1980s- and 1990s-like ( 80sSR, 80sHR, 90sSR, 90sHR), relapse/transplant ( R/BMT). Cumulative incidence and standardized mortality ratios (SMR) were calculated. Piecewise exponential and log-binomial models estimated rate ratios (RR) with 95% confidence intervals (CI). Results: Among 6148 survivors (median age 31.5 yrs), 15-yr cumulative incidence of all-cause mortality was 5.8% (CI 5.3-6.2) and HRM was 1.5% (1.2-1.7). Compared to 70s, HRM was lower for 90sSR and 90sHR (RR 0.1, CI 0.0-0.3; 0.2, 0.1-0.7), similar to that in the US population (SMR; CI: 90sSR 1.1; 0.6-1.9, 90sHR 1.9; 0.8-3.7). 20-yr cumulative incidence of SN was 3.5% (CI 3.1-3.9). Compared to 70s, 90sSR had lower risk of benign meningioma (RR 0.1, CI 0.0-0.3) and SMN (0.3, 0.1-0.6) with no absolute excess risk compared to the US population. 90sSR was associated with a lower risk of CHCs (Table). Conclusions: More recent risk-stratified therapy has succeeded in reducing risk of late mortality and CHCs among long-term survivors of ALL. [Table: see text]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".