Influence of Pain Distribution on Gait Characteristics in Patients With Low Back Pain
Bibliographic record
Abstract
STUDY DESIGN: In a cross-sectional study, vertical ground reaction force (GRF) during 2 speeds of walking were compared between 3 age- and sex-matched groups: back pain only (BPO) group, back pain with referred leg pain (LGP) group, and a control group. OBJECTIVE: The purpose was to evaluate the influence of pain distribution on vertical GRF of patients with low back problems during 2 walking speed conditions: preferred and fastest speeds. SUMMARY OF BACKGROUND DATA: People with low back pain often have difficulty walking. A better understanding of how pain distribution differentially affects walking will facilitate clinicians' assessment and enhance treatment in patients with low back pain problems. METHODS: All participants walked on a 7.62-m walkway. Vertical GRF parameters were recorded during stance phase using a force platform for each walking speed condition. Multivariate analysis of covariance was used for statistical analysis, with gait velocity as the covariate. RESULTS: The BPO and control groups did not differ significantly in vertical GRF during both walking speed conditions (P > or = 0.11). All vertical GRF parameters of the LGP group, except the peak loading force (P = 0.374), were significantly less than those of the control group during preferred walking speed condition (P < or = 0.008). However, there was no significant difference in the vertical GRF components between LGP and control groups during the fastest walking speed condition (P > or = 0.07). CONCLUSIONS: Pain distribution of people with low back problems differentially influences the vertical GRF they experience during walking. When walking at preferred speed, those with referred leg pain seem to use additional strategies besides walking slowly to attenuate the amount of force imposed on their painful leg. When challenged to walk at their fastest speed, people with back pain only walk as fast and withstand comparable amount of force as their pain-free counterparts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".