Can Functional Capacity Tests Predict Future Work Capacity in Patients With Whiplash-Associated Disorders?
Bibliographic record
Abstract
OBJECTIVE: To determine whether functional capacity evaluation (FCE) tests predict future work capacity (WC) of patients with whiplash-associated disorders (WADs) grades I and II who did not regain full WC 6 to 12 weeks after injury. DESIGN: Prospective cohort study. SETTING: Rehabilitation center. PARTICIPANTS: Workers (N=267) listed on workers' compensation with grade I or II WADs 6 to 12 weeks after injury. INTERVENTIONS: Patients performed 8 work-related FCE tests. MAIN OUTCOME MEASURES: WC (0-100%) measured at baseline and 1, 3, 6, and 12 months after testing. Correlation coefficients between FCE tests and WC were calculated. A linear mixed-model analysis was used to assess the association between FCE and future WC. RESULTS: Mean ± SD WC increased over time from 20.8%±27.6% at baseline to 32.3%±38.4%, 51.3%±42.8%, 65.6%±42.2%, and 83.2%±35.0% at the 1-, 3-, 6-, and 12-month follow-ups, respectively. Correlation coefficients between FCE tests and WC ranged from r=.06 (lifting low at 12-mo follow-up) to r=.39 (walking speed at 3mo). Strength of the correlations decreased over time. FCE tests did not predict WC at follow-up. The predictors of WC were ln (time) (β=23.74), mother language (β=5.49), WC at baseline (β=1.01), and self-reported disability (β=-.20). Two interaction terms, ln (time) × WC (β=-.19) and ln (time) × self-reported disability (β=-.21), were significant predictors of WC. CONCLUSIONS: FCE tests performed within 6 to 12 weeks after WADs injury grades I and II are associated with WC at baseline but do not predict future WC, whereas time course, mother language, WC at baseline, and self-reported disability do predict future WC. Additionally, the interaction between time course WC at baseline and self-reported disability predicted future WC.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".