Force Production Parameters in Patients With Low Back Pain and Healthy Control Study Participants
Bibliographic record
Abstract
STUDY DESIGN: A control group study with repeated measures. OBJECTIVE: To compare isometric force production parameters in low back pain and healthy study participants. SUMMARY AND BACKGROUND DATA: Recent evidence suggests that chronic patients with low back pain exhibit deficits in trunk proprioception and motor control. The control of force and its between-trial variability are often taken as critical determinants of performance. We compared various force time characteristics in patients with low back pain and healthy study participants. METHODS: Fifteen control study participants and 16 patients with low back pain participated in this study. Study participants were required to exert 50% and 75% of the maximal trunk flexion and extension. In a learning phase, visual and verbal feedback was provided. Following these learning trials, study participants were asked to perform 10 trials without any feedback. Time to peak force, time to peak force variability, peak force variability, and absolute error in peak force were calculated. Time to peak and peak dF/dt were computed to determine if the first peak of dF/dt could predict the peak force achieved. RESULTS: Two subgroups of patients with low back pain were identified. Controls and patients with low back pain with more pain showed faster time to peak force than patients with low back pain with less pain (331 ms and 341 ms vs. 574 ms, respectively). Linear regressions showed that, for control study participants and low back pain study participants with more pain, peak dF/dt explained 94.0% and 97.0% of the variance observed in peak force while 84.4% was explained for low back pain study participants with less pain. Peak force variability and absolute error in peak force were similar for all groups. CONCLUSIONS: Patients with low back pain were able to produce isometric forces with an accuracy similar to control study participants. The longer time to peak force and the smaller percentage of variance observed for the linear regressions suggest that some patients with low back pain adopted a control mode that was less "open-loop." It is possible that this mode of producing forces results from an adaptation to chronic pain or tissue degeneration.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".