Integration of primary contact physiotherapists in the emergency department for individuals presenting with minor musculoskeletal disorders: Protocol for an economic evaluation
Bibliographic record
Abstract
OBJECTIVES: 1) To compare the average cost of an emergency department (ED) visit for various minor musculoskeletal disorders between two models of care (physiotherapist and ED physician or ED physician alone); 2) To evaluate the incremental cost-effectiveness ratio (ICER) of these two models of care over a 3-month period post-initial visit; and 3) To estimate the ICER of three ED models of care (physiotherapist and ED physician, ED physician alone, physiotherapist alone) over a two-year period. METHODS: Obj.1: The costs incurred by participants in the two groups during their ED visit will be calculated using the Time-Driven Activity-Based Costing (TDABC) method. These costs will be compared using generalized linear models. Obj. 2: The ICER of the two models will be evaluated over three months via a cost-utility analysis that will combine costs and effectiveness data (quality-adjusted life years) using both Health system and Societal perspectives (patient + health system costs). Obj. 3: The 2-year ICER of the three above-mentioned models will be estimated using a mathematical model including a decision tree (0-3 months post-visit) and a Markov model (3-24 months post-visit), also using both Health system and Societal perspectives. Data to answer the three objectives will come from data collected during a randomized clinical trial (n = 78, CHU de Québec)which will be supplemented with data obtained via some of the CHU de Québec administrative databases (nominative data; SIURGE (ED management software), Cristal-Net (patient electronic record), and the ED's pharmacy transactions directory; administrative data: drug costs repository), the literature, and public cost repositories. CONCLUSION: This study will help to determine which model of care is most efficient for the management of individuals who come to the ED with minor musculoskeletal disorders. The increased involvement of various health professionals in the management of patients in the ED paves the way for the development of new avenues of practice and more efficient organization of services.
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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 | 0.036 | 0.032 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.061 | 0.007 |
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".