Les impacts de la chimiothérapie sur la fonction musculaire de l’enfant atteint de cancer
Bibliographic record
Abstract
Background: Anticancer treatments, especially chemotherapy, can negatively impact muscular function in children cancer. These alterations may include loss of muscle mass, strength or joint flexibility. Objective : This literature review aimed to explore the effects of chemotherapy on muscular function in pediatric cancer patients. Method: A search was conducted in PubMed and Cochrane databases between September 2024 and March 2025. Four observational studies were selected : one case-control study, one cohort, one cross-sectional study, and one retrospective study. Risk of bias was assessed using the Newcastle-Ottawa Scale (NOS). Results: The included studies showed a tendency toward decreased muscle mass, strength and quality of life during or after chemotherapy. However, heterogeneity in methodology, small sample sizes and non-standardized tools limit the generalizability of the findings. Discussion: While a causal link between chemotherapy an impaired muscle function cannot be definitively established, this review highlights the importance of early physiotherapy involvement and systematic functional assessment. Further research is needed to strengthen the level of evidence on this issue.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".