An assessment of gait analysis in the rehabilitation of children with walking difficulties
Bibliographic record
Abstract
PURPOSE: To assess the current status of computerized gait analysis techniques in the management of children with cerebral palsy or spina bifida who have significant walking disorders. METHOD: Synthesis of available data from a review of the literature, drawing on MEDLINE, EMBASE, PRE-MEDLINE, HealthStar and PsychInfo. Other information was obtained from persons with expertise in computerized gait analysis. Cost data were obtained from Canadian rehabilitation centres and the provincial health ministry. RESULTS: This technology seems helpful in detecting gait changes. However, available evidence is insufficient to draw conclusions about the influence of computerized gait analysis on treatment outcomes. Part of the rationale for use of the technology is that costs of gait analysis (of the order of $ CAN 2,000 per examination) would be offset by a decrease in follow-up surgical procedures and associated hospital care. There could also be a major influence on children's independence and quality of life. However, there are as yet no convincing data to support these propositions. CONCLUSIONS: Computerized gait analysis is a potentially useful technology in the management of children with walking disabilities, but its efficacy is not established. It should be regarded as a developing technology and its clinical application linked to systematic collection and assessment of outcomes data.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".