Screening for asymptomatic diabetes and metabolic comorbidities in pediatric patients during therapy for acute lymphoblastic leukemia
Bibliographic record
Abstract
OBJECTIVES: Chronic metabolic disturbances related to cancer treatment are well reported among survivors of pediatric acute lymphoblastic leukemia (ALL). However, few studies have investigated the incidence of these complications during the phase of chemotherapy. We evaluated the incidence of acute metabolic complications occurring during therapy in our cohort of patients diagnosed with ALL. METHODS: A prospective study involving 50 ALL pediatric patients diagnosed and treated between 2012 and 2016 in our oncology unit. We collected weight, blood pressure, fasting plasma glucose and hemoglobin A1C (HBA1c) levels during the two years of therapy. RESULTS: Obesity and overweight occurred in 43 and 25%, respectively among patients and have been reached at 12 months of chemotherapy. About 26% of the patients developed high blood pressure and 14% experienced hyperglycemias without meeting diabetes criteria. There was a significant decrease of HBA1c levels between the beginning and the end of therapy (p<0.0001). CONCLUSIONS: Increase of body mass index in our ALL pediatric patients occurred during the first months of therapy and plateaued after a year of treatment. We should target this population for early obesity prevention. HbA1c levels measured during therapy did not reveal diabetes criteria. Hence, fasting blood glucose levels are sufficient to monitor ALL pediatric patients' glycemia.
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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 | 0.001 |
| 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 | 0.000 |
| 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".