Musculoskeletal manifestations of childhood malignancies: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Timely diagnosis of malignancies in children is very important. Knowing the clinical manifestations of malignancies help in timely diagnosis. The aim of this study is to investigate the prevalence of musculoskeletal manifestations of malignancies in children. METHODS: we searched the manuscript databases including Scopus, Web of science, Google scholar, Medline, and Cochrane for all studies in accordance with the relevant keywords. The nine-star Newcastle-Ottawa Scale (NOS) scoring system was employed to assess the methodological quality of all eligible studies. Statistical analysis was performed using the Comprehensive Meta-Analysis (CMA) software. RESULTS: Of 96 articles initially collected by database searching, 13 articles were eligible for the final analysis. The major musculoskeletal manifestations related to childhood malignancies include bone pain, bone swelling, bone tenderness, bone fracture, vertebral collapses, joint effusion and joint tenderness. Manifestations related to these malignancies also include septic arthritis-type symptoms, osteomyelitis-type symptoms, and osteomyelitis. The overall pooled prevalence of musculoskeletal manifestations in children suffering hematopoietic tumors (acute lymphoblastic leukemia and acute myeloid leukemia) was 32.1% (95%CI: 24.0-41.3%). The overall prevalence of musculoskeletal manifestations due to neuroblastoma was also 30.5% (95%CI: 19.2-44.9%). The rate of musculoskeletal manifestations due to other childhood cancers range from 23.5 to 80.3%. CONCLUSION: A significant part of childhood malignancies are associated with musculoskeletal manifestations. Clinicians should carefully evaluate and closely follow children with musculoskeletal manifestations to timely diagnosing of malignancies. TRIAL REGISTRATION NUMBER: Not applicable.
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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.036 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".