Development of a Cumulative Psychosocial Factor Index for Problematic Recovery Following Work-Related Musculoskeletal Injuries
Bibliographic record
Abstract
BACKGROUND: Psychosocial variables such as fear of movement, depression, and pain catastrophizing have been shown to be important prognostic factors for a wide range of pain-related outcomes. The potential for a cumulative relationship between different elevated psychosocial factors and problematic recovery following physical therapy has not been fully explored. OBJECTIVE: The purpose of this study was to determine whether the level of risk for problematic recovery following work-related injuries is associated with the number of elevated psychosocial factors. DESIGN: This was a prospective cohort study. METHODS: Two hundred two individuals with subacute, work-related musculoskeletal injuries completed a 7-week physical therapy intervention and participated in testing at treatment onset and 1 year later. An index of psychosocial risk was created from measures of fear of movement, depression, and pain catastrophizing. This index was used to predict the likelihood of experiencing problematic recovery in reference to pain intensity and return-to-work status at the 1-year follow-up. RESULTS: Logistic regression analysis revealed that the number of prognostic factors was a significant predictor of persistent pain and work disability at the 1-year follow-up. Chi-square analysis revealed that the risk for problematic recovery increased for patients with elevated levels on at least 1 psychosocial factor and was highest when patients had elevated scores on all 3 psychosocial factors. LIMITATIONS: The physical therapy interventions used in this study were not standardized. This study did not include a specific measure for physical function. CONCLUSIONS: The number of elevated psychosocial factors present in the subacute phase of recovery has a cumulative effect on the level of risk for problematic recovery 1 year later. This research suggests that a cumulative prognostic factor index could be used in clinical settings to improve prognostic accuracy and to facilitate clinical decision making.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".