Moving in pain ‐ A preliminary study evaluating the immediate effects of experimental knee pain on locomotor biomechanics
Bibliographic record
Abstract
Pain changes how we move, but it is often confounded by other factors due to disease or injury. Experimental pain offers an opportunity to isolate the independent effect of pain on movement. We used cutaneous electrical stimulation to induce experimental knee pain during locomotion to study the short-term motor adaptions to pain. While other models of experimental pain have been used in locomotion, they lack the ability to modulate pain in real-time. Twelve healthy adults completed the single data collection session where they experienced six pain intensity conditions (0.5, 1, 2, 3, 4, 5 out of 10) and two pain delivery modes (tonic and phasic). Electrodes were placed over the lateral infrapatellar fat pad and medial tibial condyle to deliver the 10 Hz pure sinusoid via a constant current electrical stimulator. Pain intensity was calibrated prior to each walking bout based on the target intensity and was recorded using an 11-point numerical rating scale. Knee joint angles and moments were recorded over the walking bouts and summarized in waveform and discrete outcomes to be compared with baseline walking. Knee joint angles changed during the swing phase of gait, with higher pain intensities resulting in greater knee flexion angles. Minimal changes in joint moments were observed but there was a consistent pattern of decreasing joint stiffness with increasing pain intensity. Habituation was limited across the 30-90 second walking bouts and the electrical current needed to deliver the target pain intensities showed a positive linear relationship. Experimental knee pain shows subtle biomechanical changes and favourable habituation patterns over short walking bouts. Further exploration of this model is needed in real-world walking conditions and over longer timeframes to quantify motor adaptations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".