The Influence of Pain Distribution on Walking Velocity and Horizontal Ground Reaction Forces in Patients with Low Back Pain
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Bibliographic record
Abstract
Objective. The primary purpose of this paper was to evaluate the influence of pain distribution on gait characteristics in subjects with low back problems (LBP) during walking at preferred and fastest speeds. Design. Cross-sectional, observational study. Setting. Gait analysis laboratory in a health professions university. Participants. A convenience age- and gender-matched sample of 20 subjects with back pain only (BPO), 20 with referred leg pain due to back problems (LGP), and 20 pain-free individuals (CON). Methods and Measures. Subjects completed standardized self-reports on pain and disability and were videotaped as they walked at their preferred and fastest speeds along a walkway embedded with a force plate. Temporal and spatial gait characteristics were measured at the midsection of the walkway, and peak medial, lateral, anterior, and posterior components of horizontal ground reaction forces (hGRFs) were measured during the stance phase. Results. Patients with leg pain had higher levels of pain intensity and affect compared to those with back pain only (t = 4.91, P < .001 and t = 5.80, P < 0.001, resp.) and walking had an analgesic effect in the BPO group. Gait velocity was highest in the control group followed by the BPO and LGP group and differed between groups at both walking speeds (F(2.57) = 13.62, P < .001 and F(2.57) = 9.09, P < .001, for preferred and fastest speed condition, resp.). When normalized against gait velocity, the LGP group generated significantly less lateral force at the fastest walking speed (P = .005) and significantly less posterior force at both walking speeds (P ≤ .01) compared to the control group. Conclusions. Pain intensity and distribution differentially influence gait velocity and hGRFs during gait. Those with referred leg pain tend to utilize significantly altered gait strategies that are more apparent at faster walking speeds.
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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 | 0.006 | 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 it