Matching walking speed of controls affects identification of gait deviations in patients with a total knee replacement
Bibliographic record
Abstract
BACKGROUND: The assessment of functional recovery of patients after a total knee replacement includes the quantification of gait deviations. Comparisons to comfortable gait of healthy controls may incorrectly suggest biomechanical gait deviations, since the usually lower walking speed of patients already causes biomechanical differences. Moreover, taking peak values as parameter might not be sensitive to actual differences. Therefore, this study investigates the effect of matching walking speed and full-waveform versus discrete analyses. METHODS: Gait biomechanics of 25 knee replacement patients were compared to 22 controls in two ways: uncorrected and corrected for walking speed employing principal component analyses, to reconstruct control gait biomechanics at walking speeds matched to the patients. Ankle, knee and hip kinematics and kinetics were compared over the full gait cycle using statistical parametric mapping against using peak values. FINDINGS: All joint kinematics and kinetics gait data were impacted by applying walking speed correction, especially the kinetics of the knee. The lower control walking speeds used for reference generally reduced the magnitude of differences between patient and control gait, however some were enlarged. Full-waveform analysis identified greater deviating gait cycle regions beyond the peaks, but did not make peak value analyses redundant. INTERPRETATION: Matching walking speed of controls affects identification of gait deviations in patients with a total knee replacement, reducing deviations confounded by walking speed and revealing hidden gait deviations related to possible compensations. Full-waveform analysis should be used along peak values for a comprehensive quantification of differences in gait biomechanics.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".