Normative kinematics of reaching and dexterity tasks: moving towards a quantitative baseline for Functional Capacity Evaluations (FCEs)
Bibliographic record
Abstract
Purpose: This work generates a comprehensive description of upper extremity and torso kinematics of a healthy population during reaching and dexterity Functional Capacity Evaluation (FCE) tasks. Methods: Upper limb and torso kinematic data were collected from 30 young, healthy participants as they performed three common FCE tasks: repetitive reaching, fingertip dexterity, and hand and forearm dexterity. Kinematic profiles were created for all clinically relevant angles of the torso, shoulder, elbow, and wrist. Results: These provocative tasks require large ranges of motion and create high demand postures for the upper limb, specifically at the shoulder. Arm elevation was up to 90°, while humeral internal rotation of 25° was observed. Torso angles were typically below 30° from neutral and elbow flexion remained within 90°–120° for nearly all tasks. Wrist ulnar deviation ranged from 0° to 26° for both wrists. Conclusion: The normative data created in this investigation provide a description of healthy motion during reaching and dexterity tasks. These normative curves are the initial step towards understanding movement that would contraindicate return to work during an FCE. This work supports a future clinical goal of being able to identify persons at risk of further injury or disability if returned to work too early.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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".