The effectiveness of interventions to treat hypothalamic obesity in survivors of childhood brain tumours: a systematic review
Bibliographic record
Abstract
BACKGROUND: Survivors of childhood brain tumours (SCBT) are at risk of type 2 diabetes and cardiovascular diseases. Obesity is a major driver of cardiometabolic diseases in the general population, and interventions that tackle obesity may lower the risk of these chronic diseases. The goal of this systematic review was to summarize current evidence for the presence of interventions to manage obesity, including hypothalamic obesity, in SCBT. METHODS: The primary outcome of this review was the body mass index z-score change from baseline to the end of the intervention and/or follow-up. Literature searches were conducted in PsycINFO, CINAHL, the Cochrane Library, Medline, SPORTDiscus, EMBASE and PubMed. Two reviewers completed study evaluations independently. RESULTS: Eleven publications were included in this systematic review (lifestyle intervention n = 2, pharmacotherapy n = 6 and bariatric surgery n = 3). While some studies demonstrated effectiveness of interventions to manage obesity in SCBT and alter markers of obesity and cardiometabolic risk, the evidence base was limited and of low quality, and studies focused on hypothalamic obesity. We conclude that there is urgent need to conduct adequately powered trials of sufficient duration, using existing and novel therapies to manage obesity, reduce the burden of cardiometabolic disorders and improve outcomes in SCBT.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".