Assessing a whiplash management model: a population-based non-randomized intervention study.
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effectiveness of a multidisciplinary clinical management approach for whiplash-associated disorders (WAD) following a motor vehicle injury in Quebec. METHODS: A clinical management model was implemented in 5 geographic regions of the Province of Quebec, Canada, in 7 hospitals and 19 clinics. A 2-group population-based parallel design was used to assess its effectiveness. All patients with a new whiplash injury seen in these 26 centers between March and September, 2001 were entered into the Whiplash Management Model (experimental group). A reference group included all subjects who had a whiplash injury during this same period but were not seen in these 26 intervention centers. All subjects were followed for up to a year. The outcome variables were time on compensation, time to file closure, and total direct costs. RESULTS: A total of 288 patients with WAD were identified in the experimental group and 1,875 patients in the reference group. The rate of ending of compensation was significantly higher in patients who received the experimental treatment model than those receiving the reference treatment approach (rate ratio, RR: 3.2; 95% confidence interval, CI: 2.8-3.6). The rate of file closure was also significantly higher with the experimental treatment (RR: 1.5; 95% CI: 1.2-1.8). The average cost per patient was significantly reduced with the experimental intervention. CONCLUSION: A coordinated whiplash management approach can lead to earlier return to work and lower costs for patients who have sustained a whiplash injury.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".