Functional Capacity Evaluation in Different Societal Contexts: Results of a Multicountry Study
Bibliographic record
Abstract
Purpose To examine factors associated with Functional Capacity Evaluation (FCE) results in patients with painful musculoskeletal conditions, with focus on social factors across multiple countries. Methods International cross-sectional study was performed within care as usual. Simple and multiple multilevel linear regression analyses which considered measurement's dependency within clinicians and country were conducted: FCE characteristics and biopsychosocial variables from patients and clinicians as independent variables; and FCE results (floor-to-waist lift, six-minute walk, and handgrip strength) as dependent variables. Results Data were collected for 372 patients, 54 clinicians, 18 facilities and 8 countries. Patients' height and reported pain intensity were consistently associated with every FCE result. Patients' sex, height, reported pain intensity, effort during FCE, social isolation, and disability, clinician's observed physical effort, and whether FCE test was prematurely ended were associated with lift. Patient's height, Body Mass Index, post-test heart-rate, reported pain intensity and effort during FCE, days off work, and whether FCE test was prematurely ended were associated with walk. Patient's age, sex, height, affected body area, reported pain intensity and catastrophizing, and physical work demands were associated with handgrip. Final regression models explained 38‒65% of total variance. Clinician and country random effects composed 1-39% of total residual variance in these models. Conclusion Biopsychosocial factors were associated with every FCE result across multiple countries; specifically, patients' height, reported pain intensity, clinician, and measurement country. Social factors, which had been under-researched, were consistently associated with FCE performances. Patients' FCE results should be considered from a biopsychosocial perspective, including different social contexts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".