Does Multidisciplinary Rehabilitation Benefit Whiplash Recovery?
Bibliographic record
Abstract
In Brief Study Design. Population-based, incidence cohort. Objectives. To evaluate a government policy of funding community and hospital-based fitness training and multidisciplinary rehabilitation for whiplash. Summary of Background Data. Although insurance benefits commonly include rehabilitation for whiplash, its effectiveness is unknown. Methods. All Saskatchewan adults treated for whiplash (n = 6,021) over a 2-year period were followed up at 6 weeks, 3, 6, 9, and 12 months. Recovery was defined by self-report of improvement. Recovery times were compared between those attending fitness training at health clubs (n = 833), multidisciplinary outpatient rehabilitation (n = 468), and multidisciplinary inpatient rehabilitation (n = 135) to those receiving usual insured individual care. Results. Recovery was 32% slower in those receiving fitness training within 69 days of injury (P = 0.001) and 19% slower when received within 119 days of injury (P = 0.041). Recovery was 50% slower in those receiving outpatient rehabilitation within 119 days of injury (P = 0.001). Attending inpatient rehabilitation did not influence recovery rates during the follow up (P = 0.131). Multivariable adjustment for important prognostic factors did not change these results. Conclusions. We found no evidence to support the effectiveness of a population-based program of fitness training and multidisciplinary rehabilitation for whiplash. Rehabilitation programs should be tested in randomized trials before being recommended to injured populations. The effectiveness of group fitness training and multidisciplinary outpatient and inpatient rehabilitation has not been proven for whiplash injuries. Our evaluation of a province-wide program of community and hospital-based fitness training and multidisciplinary rehabilitation showed that outcomes were not better than the usual individual care provided by primary care practitioners.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".