Prevalence and Risk Factors for Metabolic Syndrome Among Childhood Acute Lymphoblastic Leukemia Survivors: Experience From South India
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Improved survival of childhood acute lymphoblastic leukemia (ALL) has diverted attention to the long-term consequences of the treatment; metabolic abnormalities being one of the most important issues. METHODS: Children diagnosed with ALL at age 14 years and younger at Regional Cancer Centre in South India who completed treatment and who were on follow-up for >2 years were enrolled in the study between April 1, 2018 and March 31, 2019. They were prospectively evaluated for the presence of metabolic syndrome (MS) and associated risk factors. RESULTS AND DISCUSSION: A total of 277 survivors of pediatric ALL were recruited during the study period. MS was present in 8.3% (n=23) and 6% (n=13) survivors by National Cholesterol Education Programme Adult Treatment Panel III (NCEPATP III) and International Diabetes Federation (IDF) criteria, respectively. The prevalence of overweight and obesity in the survivors was 9% and 13%. The prevalence of increased waist circumference, low high-density lipoprotein cholesterol, elevated triglycerides, elevated fasting glucose, and increased blood pressure were 10.5%, 28.9%, 24.9%, 2.5%, and 9%, respectively. Overweight/obese survivors were at an increased risk for developing MS (odds ratio=17.66; 95% confidence interval=6.2-50.16, P=0.001). Survivors who received cranial radiotherapy were at an elevated risk for having low high-density lipoprotein cholesterol (P=0.001). CONCLUSIONS: In our study, the prevalence of MS was higher in childhood ALL survivors, as compared with the general population. The study points to the need for regular screening of pediatric ALL survivors for early detection of MS, along with lifestyle modification in those with metabolic abnormalities, to curb the growing incidence of coronary artery disease.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".