Task intensity influences upper limb and torso kinematics during two common overhead Functional Capacity Evaluation tasks
Bibliographic record
Abstract
BACKGROUND: The Functional Capacity Evaluation (FCE) is a tool used in the return-to-work process to guide treatment and decision making. Individual abilities and maximum capacity can be determined through visual observations of changes in mechanics as intensity increases. OBJECTIVE: The purpose of this study was to determine kinematic differences between sexes and intensity levels of two common FCE tasks to establish normative behaviours. METHODS: Upper limb and torso kinematics were collected from 30 participants as they performed the overhead lift and overhead work FCE tasks. Mean, maximum, and minimum values were calculated for clinically relevant joint angles. Mean and maximum segment velocity was also calculated and each variable was tested with a mixed model ANOVA. RESULTS: During the overhead lift task, maximum torso flexion and maximum torso extension increased from the lightest to the heaviest load. Humeral flexion angle at the beginning of the lift and wrist ulnar deviation also increased with load. Torso extension, humeral flexion and axial rotation, and wrist extension all increased with time during the overhead work task. CONCLUSIONS: Increasing intensity during the overhead tasks influenced kinematic variables. These observable changes can be used by evaluators to more reliably determine safe maximum capacities for each patient and identify compensatory actions.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".