Medication-induced diabetes during induction treatment for ALL, an early marker for future metabolic risk?
Bibliographic record
Abstract
Medication-induced diabetes (MID) is seen in children treated for acute lymphoblastic leukemia (ALL) mostly during induction, due to the use of l-asparaginase and glucocorticoids. Our objective was to assess whether MID during induction, is a risk factor for future impaired glucose tolerance (IGT), diabetes, or metabolic syndrome. Ninety survivors of pediatric ALL, ages 10 yr and older were recruited, 30 with history of MID and 60 controls. Waist/height ratio >0.5 was considered as an increased risk for central adiposity and insulin resistance. Lipid profile and an oral glucose tolerance test (OGTT) were performed. Study patients were older than controls (17.2 vs. 14.9, p < 0.05). The groups had similar sex distribution, body mass index (BMI) z-score, and Tanner staging. A waist/height ratio of >0.5 was seen in 60 and 31.7% of the study and control groups, respectively (p = 0.01). Increased frequency of IGT in the study group compared with the control group was seen (13.3 and 1%, respectively) (p = 0.07). We observed a trend toward higher proportion of patients with multiple features of metabolic syndrome in the study compared with control group (16.7 vs. 5%, p = 0.09). In conclusion, MID during induction may be an early marker for metabolic disturbances later in life. The higher rates of increased waist/height ratio, and subjects with multiple metabolic syndrome features, may predict a metabolic risk in children with history of MID. Rates of IGT were four fold higher in the study group although not statistically significant. MID may be a 'red flag' indicating the need for ongoing metabolic screening and lifestyle modifications to prevent future metabolic 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".