Impact of Risk-Stratified Therapy on Health Status in Survivors of Childhood Acute Lymphoblastic Leukemia: A Report from the Childhood Cancer Survivor Study
Bibliographic record
Abstract
Abstract Background: Prior studies have identified that survivors of childhood acute lymphoblastic leukemia (ALL) report poor health status. It is unknown how risk-stratified therapy impacts the health status of ALL survivors. Methods: We estimated and compared the prevalence of self-reported poor health status among adult (≥18 years) survivors of childhood ALL diagnosed at age <21 years from 1970 to 1999 and sibling controls, excluding proxy reports. Therapy combinations defined treatment groups representative of 1970s therapy (70s), standard- and high-risk 1980s and 1990s therapy (80sSR, 80sHR, 90sSR, 90sHR), and relapse/bone marrow transplant (R/BMT). Log-binomial models, adjusted for clinical and demographic factors, compared outcomes between groups using prevalence ratios (PR) with 95% confidence intervals (CI). Results: Among 5,119 survivors and 4,693 siblings, survivors were more likely to report poor health status in each domain including poor general health (13.5% vs. 7.4%; PR = 1.92; 95% CI, 1.69–2.19). Compared with 70s, 90sSR and 90sHR were less likely to report poor general health (90sSR: PR = 0.75; 95% CI, 0.57–0.98; 90sHR: PR = 0.58; 95% CI, 0.39–0.87), functional impairment (90sSR: PR = 0.56; 95% CI, 0.42–0.76; 90sHR: PR = 0.63; 95% CI, 0.42–0.95), and activity limitations (90sSR: 0.61; 95% CI, 0.45–0.83; 90sHR: PR = 0.59; 95% CI, 0.38–0.91). An added adjustment for chronic conditions in multivariable models partially attenuated 90sSR risk estimates. Conclusions: Risk-stratified ALL therapy has succeeded in reducing risk for poor general health, functional impairment, and activity limitations among more recent survivors of standard- and high-risk therapy. Impact: Future research into the relationship between risk-stratified therapy, health status, and late health outcomes may provide new opportunities to further improve late morbidity among survivors.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".