Predicting Timely Recovery and Recurrence Following Multidisciplinary Rehabilitation in Patients With Compensated Low Back Pain
Bibliographic record
Abstract
In Brief Study Design. Historical cohort study. Objectives. We investigated factors predictive of timely and sustained recovery following multidisciplinary rehabilitation in Workers’ Compensation claimants with low back pain. Summary of Background Data. It is still unknown which factors predict better outcomes among back pain patients enrolled in intensive rehabilitation programs. Previously, few consistent predictors have been reported. Methods. We created and tested predictive models using data from clinical and administrative databases of the Alberta Workers’ Compensation Board. Predictive models were built on a cohort of subjects admitted for multidisciplinary rehabilitation in 1999 and tested on subjects admitted in 2000. Cox regression was used to evaluate days to time-loss benefit suspension and days to claim closure following admission for rehabilitation. Logistic regression was used to evaluate risk of future recurrence as judged through time-loss benefit resumption, claim reopening, or new back-related claims filing. Results. Prediction models were variable between exploratory and confirmatory stages, and few variables were found to predict consistently. The number of preadmission healthcare visits was the most robust predictor of all recovery outcomes. Recurrence rates were 18% in 1999 and 22% in 2000. A higher number of preadmission healthcare visits and more previous back-related claims were associated with higher risk of recurrence. Conclusions. The number of preadmission healthcare visits was the most robust prognostic indicator with more healthcare visits related to delayed recovery and higher risk of recurrence. Recurrence rates following successful functional restoration were consistent with the episodic and recurrent nature of low back pain. A historical cohort study was conducted of factors predicting outcome following multidisciplinary rehabilitation for claimants with back pain. More preadmission healthcare visits were the most robust indicator of delayed recovery recurrence. Recurrence rates were slightly higher than previously reported consistent with the episodic nature of back pain.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".