Treatment Outcomes for Workers Compensation Patients in a US‐Based Interdisciplinary Pain Management Program
Bibliographic record
Abstract
OBJECTIVES: Assess the efficacy of an outpatient-based interdisciplinary pain rehabilitation program for patients with active workers compensation claims. PATIENTS: Data were available for 101 patients, primarily with chronic low back pain (75%), who participated in the program. METHODS: Treatment included a 4-week (Monday to Friday), 8-hours/day graded progressive program that included individual and group therapies (pain psychology, physical therapy, occupational therapy, relaxation training/biofeedback, aerobic conditioning, pool therapy, vocational counseling, patient education and medical management). Outcome measures included program completion status, release-to-work status, return-to-work status, total scores on the Beck depression inventory, state-trait anxiety inventory, pain catastrophizing scale, and the McGill pain questionnaire visual analogue scale (MPQ VAS). The majority of the patients (65%) graduated from the program. Pre-postoutcome data were available for those who graduated from the program. For noncompleters, last obtained MPQ VAS was compared with their initial MPQ VAS scores. RESULTS: Of those completing the program, most patients (91%)were released to return to work; with 80% released to full-time status and 11% released to gradual return. Approximately half (49%) of the program completers returned to work. Paired-samples t-tests showed that program completers had significant reductions in depression (P = 0.000), pain-related catastrophizing (P = 0.033), and pain intensity (P = 0.000), but not in anxiety (P = 0.098). Interestingly, the last obtained (at early discharge/withdrawal) pain intensity scores (M = 70.33) were higher than at baseline (M = 61.20) in the noncompleters. This difference was not statistically significant (P = 0.127) but may be clinically meaningful. DISCUSSION: Our results support the efficacy of an outpatient-based 4-week interdisciplinary pain rehabilitation program in decreasing emotional distress, reducing pain intensity, and improving return-to-work status in the majority of completers in this challenging population. Patients reporting increased pain at discharge or those discharged early may have been due to operant factors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".