The Sensitivity and Specificity of Functional Capacity Evaluations in Determining Maximal Effort
Bibliographic record
Abstract
STUDY DESIGN: Randomized trial. OBJECTIVES: To determine the sensitivity and specificity of maximal effort testing in functional capacity evaluations. SUMMARY OF BACKGROUND DATA: Functional capacity evaluations are widely used to determine when an injured worker is able to return to work. The accurate assessment of function is dependent on a patient's willingness to exert maximal effort during evaluation. Although many tests are used to suggest the presence of maximal or submaximal effort, it is unclear whether these tests can actually do what they are hoped to do. METHODS: Ninety study participants with low back pain were randomized into either a 100% effort group or a 60% effort group. After a thorough evaluation, the blinded tester was asked to give an overall opinion as to whether or not the participant was giving 100% effort or 60% effort. RESULTS: The tester's opinion on maximal effort tests within the functional capacity evaluation had an overall specificity of 84.1% and a sensitivity of 65.2%. Only 5 of 17 commonly used maximal effort tests were able to individually differentiate between maximal and submaximal effort. The final logistic regression model was able to find three covariates with reasonable explanation of the proportion of variance in the outcome variable of effort (R 3 0.444) with goodness of fit. CONCLUSIONS: The determination of maximal effort in a functional capacity evaluation is complex. Because of the wide-ranging medicolegal and ethical considerations, caution is recommended in the labeling of patients as exerting either maximal or submaximal effort.
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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 | 0.026 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".