Effect of peripheral field loss on gait performance: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: The peripheral visual field provides essential environmental information for safe locomotion. Deficits in peripheral field can adversely affect gait performance and safety. This review aimed to consolidate current knowledge on the impact of peripheral field loss on gait and to identify the key parameters for gait assessment. Methods: A comprehensive systematic search was conducted across AMED, CINAHL, PubMed, Scopus, and Web of Science databases, supplemented by a manual search on Google Scholar, covering the period up to November 2023. Eligible studies examining the relationship between peripheral field loss and gait performance were summarized and methodologically assessed using the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) quality rating tool. Meta-analysis was conducted using the Comprehensive Meta-analysis (CMA) software. Results: < 0.001). In addition, peripheral field loss correlated with an increased number of collisions, indicating compromised mobility safety. Moreover, alterations in spatiotemporal gait parameters, such as stride length and cadence, were also linked to peripheral field loss. Conclusion: Peripheral field loss is significantly associated with reduced walking speeds, altered gait characteristics, and impaired mobility safety during locomotion. Future research should adopt a standardized set of gait and mobility metrics to enhance cross-study comparisons among diverse patient populations. Systematic Review Registration: CRD42022297071.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.022 |
| Bibliometrics | 0.007 | 0.008 |
| 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) | 0.004 | 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".