Interlaboratory Study Toward Combining Gait Kinematics Data Sets of Long-Distance Runners
Bibliographic record
Abstract
The limited sample size in gait studies has hampered progress in the field. This challenge could be addressed through multicenter studies, thereby leveraging data sets from different laboratories. This study compared 3-dimensional lower-extremity running kinematics between the Biomechanics and Motor Control Laboratory, Federal University of ABC (Brazil), and the Running Injury Clinic, University of Calgary (Canada). Three-dimensional lower-extremity kinematics from 23 male runners were collected from each laboratory using comparable instrumentation and experimental procedures. The 3-dimensional hip, knee, and ankle angles were compared within and between centers using root-mean-square deviation. Two-sample t tests Statistical Parametric Mapping tested the hypothesis that the data from both laboratories were not different. The sagittal plane hip, knee, and ankle angles were similar between laboratories, while notable differences were observed for frontal (hip and ankle) and transverse (hip and knee) plane angles. The average interlaboratory root-mean-square deviation (2.6°) was lower than the intralaboratory root-mean-square deviation (Biomechanics and Motor Control = 4.8°, Running Injury Clinic = 5.6°), with the ankle transverse angle displaying the smallest, and the knee transverse angle displaying the largest variability. This study demonstrates the potential of combining gait kinematics data from different laboratories to increase sample size, but frontal and transverse plane data should be considered with caution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.065 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".