Factors Influencing Results of Functional Capacity Evaluations in Workers' Compensation Claimants With Low Back Pain
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Physical and psychosocial factors are hypothesized to influence performance-based assessment. The purpose of this study was to evaluate the association between performance on the Isernhagen Work System Functional Capacity Evaluation (IWS-FCE) and various clinical and psychosocial factors. SUBJECTS: The sample consisted of 170 workers' compensation claimants who were undergoing functional capacity evaluations (FCEs) for low back injuries. METHODS: In this cross-sectional study, claimants completed a battery of work-related measures, including the IWS-FCE, the Pain Disability Index (PDI), a workplace organizational policies and practices scale, and a recovery expectations questionnaire. Functional capacity evaluation performance indicators were the number of tasks in which subjects did not meet work demands and weight lifted on the floor-to-waist lift. Analysis included multivariable regression. RESULTS: Only the PDI, pain intensity, age, and sex independently contributed to floor-to-waist lift performance. The PDI, pain intensity, and duration of injury contributed to the number of failed tasks. DISCUSSION AND CONCLUSION: The results indicate that performance on FCEs is influenced by physical factors, perceptions of disability, and pain intensity. However, perceptions of workplace organizational policies and procedures were not associated with FCE results for workers' compensation claimants with chronic back pain disability. Functional capacity evaluations should be considered behavioral tests influenced by multiple factors, including physical ability, beliefs, and perceptions.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".