Operator Bias Errors Are Reduced Using Standing Marker Alignment Device for Repeated Visit Studies
Bibliographic record
Abstract
When optical motion capture is used for motion analysis, reflective markers or a digitizer are typically used to record the location of anatomical landmarks identified through palpation. The landmarks are then used to construct anatomical coordinate systems. Failure to consistently identify landmarks through palpation over repeat tests creates artifacts in the kinematic waveforms. The purpose of this work was to improve intra- and inter-rater reliability in determining lower limb anatomical landmarks and the associated anatomical coordinate systems using a marker alignment device (MAD). The device aids the subject in recreating the same standing posture over multiple tests, and recreates the anatomical landmarks from previous static calibration trials. We tested three different raters who identified landmarks on eleven subjects. The subjects performed walking trials and their gait kinematics were analyzed with and without the device. Ankle kinematics were not improved by the device suggesting manual palpation over repeat visits is just as effective as the MAD. Intra-class correlation coefficients between gait kinematics registered to the reference static trial and registered to follow-up static trials with and without the device were improved between 1% and 33% when the device was used. Importantly, out-of-plane hip and knee kinematics showed the greatest improvements in repeatability. These results suggest that the device is well suited to reducing palpation artifact during repeat visits to the gait lab.
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 | 0.023 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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) | 0.003 | 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".