Material handling performance of patients with chronic low back pain during Functional Capacity Evaluation: A comparison between three countries
Bibliographic record
Abstract
PURPOSE: Functional Capacity Evaluations (FCEs) are batteries of tests designed to measure patients' ability to perform work-related activities. Although FCEs are used worldwide, it is unknown how patients' performances compare between countries or settings. This study was performed to explore similarities and differences in FCE performance of patients with chronic low back pain (CLBP) between three international settings that utilize the same FCE protocol. METHODS: Standardized FCEs were performed on three cohorts of patients with CLBP: A sample from an outpatient rehabilitation context in The Netherlands (n = 121), a Canadian sample in a Worker's Compensation context (n = 273), and a Swiss sample in an inpatient rehabilitation context (n = 170). Patients were undergoing FCE as part of their usual clinical care. Means and standard deviations of maximum performance on the FCE material handling items were calculated and differences compared using ANOVA. Multivariable linear regression was used to determine the relationship between country of origin and FCE performance while controlling for potential confounders including, age, sex, duration of back pain problems, and self-reported pain and disability ratings. RESULTS: Compared to the Dutch sample, the mean performance of patients in the Canadian and Swiss samples was consistently lower on all FCE items. This association remained statistically significant after controlling for potential confounders. CONCLUSIONS: Considerable differences were observed between settings in maximum weight handled on the various FCE items. Future FCE research should examine the effects of a number of potentially influential factors, including variability in evaluator judgements across settings, the evaluator-patient interaction and patients' expectations of the influence of FCE results on disability compensation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".