Cost-benefit and cost-effectiveness analysis of a disability prevention model for back pain management: a six year follow up study
Bibliographic record
Abstract
AIMS: To test the long term cost-benefit and cost-effectiveness of the Sherbrooke model of management of subacute occupational back pain, combining an occupational and a clinical rehabilitation intervention. METHODS: A randomised trial design with four arms was used: standard care, occupational arm, clinical arm, and Sherbrooke model arm (combined occupational and clinical interventions). From the Quebec WCB perspective, a cost-benefit (amount of consequence of disease costs saved) and cost-effectiveness analysis (amount of dollars spent for each saved day on full benefits) were calculated for each experimental arm of the study, compared to standard care. RESULTS: At the mean follow up of 6.4 years, all experimental study arms showed a trend towards cost benefit and cost effectiveness. These results were owing to a small number of very costly cases. The largest number of days saved from benefits was in the Sherbrooke model arm. CONCLUSIONS: A fully integrated disability prevention model for occupational back pain appeared to be cost beneficial for the workers' compensation board and to save more days on benefits than usual care or partial interventions. A limited number of cases were responsible for most of the long term disability costs, in accordance with occupational back pain epidemiology. However, further studies with larger samples will be necessary to confirm these results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".