Effect of treatment for paediatric cancers on balance: what do we know? A review of the evidence
Bibliographic record
Abstract
This review aims to explore the literature investigating balance outcomes in survivors of childhood cancer. A structured search of five databases resulted in 16 articles included in this review. Nearly all were classified as Level 4 evidence using the updated Oxford Centre for Evidence-Based Medicine Levels of Evidence. Balance abilities have been investigated solely in survivors of acute lymphoblastic leukaemia or central nervous system tumours. The literature tends to support the idea that survivors present with balance difficulties but the results need to be closely scrutinised. Several studies report results using the same experimental group, while other studies use balance outcome measures that have not had their psychometric properties assessed with this population. There are also few studies that evaluate dynamic balance abilities in survivors of paediatric cancers, which may be more influential on functional tasks. Furthermore, very few of the included studies investigate how the found balance deficits affect this population's daily lives, which would be necessary in order to determine if intervention should be geared towards this area. Directions for future research should also include multi-centred, clinically oriented trials to evaluate balance abilities in survivors of childhood cancers compared with healthy control subjects in order to strengthen the literature.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".