Current Approaches to Improve Balance in Down Syndrome Population-A Systematic Review
Bibliographic record
Abstract
Down syndrome is one of the most common chromosomal disorders in pediatrics characterized by variable intellectual disability, generalized joint laxity, and hypotonia that compromises their function and causes a delay in developing gross motor skills, poor balance, and coordination. Thus, this study aims to determine the current yet effective treatment approaches to improve balance in the Down syndrome population. The studies were explored across seven electronic databases that include MEDLINE, PubMed, Cochrane Library, Google Scholar, Scopus, PEDro, and Web of Science from inception till October 2020 comprised of experimental studies published in English language investigating the effects on balance in children and adults diagnosed with DS considering different interventions. A total of 1,570 records were retrieved from seven electronic databases published between the year's tenure of 2013-2020. 144 full-text papers were extracted to be reviewed, of which only 18 experimental studies were selected on the basis of inclusion criteria that involved 493 Down syndrome patients, investigated the effects of therapeutic exercises, manual therapy techniques, and patient-related instructions on standardized balance scales/tests. It was concluded that all the included trials demonstrated significantly profound effects in improving the static and dynamic balance of Down syndrome patients. Therefore, none of the interventions is declared as superior to another in terms of obtained results. Furthermore, these diverse interventions need to be investigated more for better understating and generalizability of outcomes.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".