Training to Improve Walking after Pediatric Spinal Cord Injury: A Systematic Review of Parameters and Walking Outcomes
Bibliographic record
Abstract
Walking or locomotor training is often initiated following pediatric spinal cord injury (SCI). There is no synthesis of the literature on interventions targeting walking for pediatric SCI, although this would assist future clinical trials and interventions. To address this need, we completed a systematic review to summarize the who, what, when, and how of walking interventions in children with SCI. Participant characteristics, training parameters, and walking outcomes with training in pediatric SCI were identified and compared with training parameters and outcomes in adults with SCI. The PubMed, Medline, AMED, Embase, PsycInfo, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and CINAHL databases were searched for studies that included participants aged 1-17 years with a SCI acquired post-birth, physical interventions, and pre- and post-training walking measures. Two researchers evaluated each study's risk of bias using a domain-based approach. Training parameters and walking outcomes were extracted. Total training duration (duration × frequency × number of weeks) was calculated. Thirteen pediatric studies (n = 43 children) were included; all but one were case series/reports. Risk of bias was high in the pediatric studies. A 2012 adult review was updated (11 studies added). As with adults, the training durations, frequencies, and modes used with the children varied; however, overground walking practice was included in 10/13 pediatric studies. Improvements in walking capacity, speed, and distance were comparable between children and adults. There was a trend for greater gains with greater total training durations. There is a paucity of high-quality research examining interventions targeting walking after pediatric SCI; however, intensive training, including practice overground, results in notable improvements.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".