Diabetes mellitus in childhood and young adult cancer survivors: A meta-analysis.
Bibliographic record
Abstract
e24101 Background: Diabetes mellitus (DM) has emerged as a possible late sequela after successfully treating pediatric and young adult (YA) malignancies. We conducted a systematic review and meta-analysis in order to quantify this risk and assess possible risk factors. Methods: We searched MEDLINE and EMBASE databases from inception to April 2019. We included cohort or case-control studies that reported the risk of DM (hazard ratio [HR], relative risk, or odds ratio, and 95% confidence interval [CI]) among childhood and YA cancer survivors ( < age 21 years) compared to those without a history of childhood or YA cancer. We used the Newcastle-Ottawa Score (NOS) to assess risk of bias and study quality. Study effect estimates were pooled using random-effects meta-analyses. Heterogeneity was assessed using the I2 statistic. Results: After applying our exclusion/inclusion criteria, we included 6 articles in our meta-analysis, with a median follow-up time of 11 (range 10-23) years. The median NOS score was 8 (range 7 – 8), indicating high study quality. The risk of DM was significantly higher in childhood and YA cancer survivors relative to controls (HR 1.66, 95% CI 1.54 to 1.79, I2 = 1%). Among cancer types, the highest risk for DM was reported after treatment for leukemia (HR 2.91, 95% CI 2.01 to 4.22 I2 = 75%), lymphoma (HR 1.63, 95% CI 1.37 to 1.94, I2 = 0%) and central nervous system malignancies (HR 1.77, 95% CI 1.30 to 2.39, I2 = 49%), relative to controls. Receiving total body irradiation or abdominal radiation was associated with a DM risk of 4.52 (95% CI 2.29 to 8.93, I2 = 87%), relative to controls. Conclusions: Our meta-analysis demonstrated an increased risk of developing DM among childhood and YA cancer survivors. More emphasis on screening for DM and prevention among this population should be offered.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.057 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".