Detecting Sarcopenic Obesity in Survivors of Pediatric Acute Lymphoblastic Leukemia: An Exploration of Body Mass Index and Triponderal Mass Index as Potential Surrogate Markers
Bibliographic record
Abstract
Survivors of pediatric acute lymphoblastic leukemia (ALL) often have altered body composition secondary to treatment effects, including sarcopenic obesity (SO), which increases the risk of both metabolic complications and frailty. SO is difficult to detect without using advanced imaging techniques to which access is often limited. To explore whether common clinical indices can reliably identify the presence of SO in a cohort of long-term survivors of ALL, the discriminatory capacity of body mass index (BMI) or triponderal mass index (TMI, kg/m 3 ) for detecting SO was assessed. Thresholds of BMI and TMI associated with overweight or obesity status had poor sensitivity (<50%) and specificity for detecting SO. Total misclassification rates at these thresholds exceeded 50% and positive likelihood ratios were nonsignificant. Notably, TMI is more strongly correlated with elevated adiposity than is BMI in this survivor population ( R2 =0.73 vs. 0.57), suggesting further exploration is warranted. Our study is limited by the sample size, precluding detailed regression analysis. This study highlights the challenges of identifying SO in survivors of pediatric ALL using common clinical indices. Prospective evaluation of additional potential surrogate markers in survivors, in conjunction with the component features of SO, should be a key focus of future research.
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".