Nutritional status, body composition and chemotherapy dosing in children and young people with cancer: a systematic review by the SIOP nutrition network
Bibliographic record
Abstract
Malnutrition (undernutrition or overweight/obesity) might significantly impact the pharmacokinetics and pharmacodynamics of antineoplastic drugs in children and adolescents (<21 years). A comprehensive systematic literature search was performed on MEDLINE (PubMed), EMBASE, Web of Science, Scopus, ProQuest, Cochrane Trials, and Cochrane Reviews. Databases were searched up to 30 September 2024. Of 4186 articles identified, 150 full texts were evaluated and 12 selected for inclusion. Eight additional articles were identified following a panel review and 6 included, resulting in a total of 18 articles for data extraction. Relevant pharmacokinetic parameters were described for mercaptopurine, vincristine, anthracyclines, methotrexate, busulfan, bevacizumab, and crizotinib. Due to the heterogeneity and limited number of studies per antineoplastic drug, formal statistical analysis or meta-analysis was not appropriate. Variations in the definition of nutritional status, dosing strategies, and type of pharmacokinetic analyses were observed; therefore, no dosing recommendations could be made. With the increasing childhood cancer burden in LMIC, high prevalence of undernutrition, and the global burden of childhood obesity, there is an urgent need for more research in this area. Prospective studies should incorporate uniform definitions and standardised pharmacological approaches to optimise treatment options for children with cancer globally. SYSTEMATIC LITERATURE REVIEW REGISTRATION: PROSPERO: (reference: CRD42023435261).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".