Errors associated with bin boundaries in observation-based posture assessment methods
Bibliographic record
Abstract
The trunk posture misclassification errors made by novice and experienced operators were quantified as a function of the angular distance from posture bin boundaries, similar to those used in observation-based posture assessment tools such as 3DMatch. The effect that these misclassification errors had on cumulative and peak low back loads was also determined in three simulated lifting scenarios. Ninety subjects in 3 experience groups were randomly presented with images of known trunk angle via a monitor. Subjects were instructed to make quick and accurate bin selections using standardized pictures included below the images on the monitor. Mean % bin misclassification errors were approximately 32% and 22% for the flexion/extension and lateral bend views, respectively. More bin classification errors were made the closer a viewed image was to a posture bin boundary, regardless of expertise level, and the number of errors made decreased as operator experience increased. Approximately 99% of bin selections were made either in the correct bin or in the bins immediately adjacent to the correct bin in both views. Misclassification errors made in the 3 simulated lifting scenarios induced errors in peak and cumulative loads in 66% of the cases assessed, with an average absolute difference of 13.5% across all load variables. Future work is aimed at determining the effect of training and bin size on the error misclassification rate for all body segments and views.
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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 | 0.010 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".