Impact of marker placement on angular kinematics in transfemoral osseointegrated prosthesis users — A longitudinal case-series study
Bibliographic record
Abstract
BACKGROUND: The accuracy of biomechanical data using a Helen Hayes model in instrumented gait analysis can be significantly affected by soft tissue artifacts (STA). RESEARCH QUESTION: How can STA be quantified and the accuracy of angular kinematics be improved in transfemoral osseointegrated prosthesis gait analysis? METHODS: To quantify STA associated with the thigh marker, we examined 12 data sets for which an implant marker was added to the Helen Hayes marker set, placed on the thigh segment of four transfemoral osseointegrated prosthesis users. This marker was located on the implant connection extending from the femur. We aimed to identify differences in the calculated range of motion (ROM) during gait when using implant, medial knee, or thigh markers. RESULTS: The results indicate significant differences in ROM for hip rotation and knee varus/valgus between markers for all but one participant (p < .05). Hip rotation differences between the thigh and implant markers exceeded 10˚ for one participant and were about 5˚ for two others. Knee varus/valgus ROM differences between markers ranged from 3˚ to 9˚ for three participants. No significant differences were found for hip flexion/extension, hip abduction/adduction, or knee flexion/extension for most participants. SIGNIFICANCE: Using an implant marker in transfemoral osseointegrated prosthesis users results in more accurate femoral tracking than using the thigh marker. Due to the similarity in angular kinematics observed between the medial knee and implant markers, the medial knee or the implant marker should be used as an alternative to the thigh marker for osseointegrated transfemoral prosthesis users.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".