Costs of physician and diagnostic imaging services for shoulder, knee, and low back pain conditions: A population-based study in Alberta, Canada
Bibliographic record
Abstract
Objectives: To 1) estimate the utilization and costs of physician and diagnostic imaging (DI) services for shoulder, knee, and low-back pain (LBP) conditions; and 2) examine determinants of the utilization and costs of these services. Methods: All patients visiting a physician for shoulder, knee, or LBP conditions (identified by the ICD-9 codes) in Alberta, Canada, in fiscal year (FY) 2022/2023 were included. Interested outcomes included numbers and costs of physician visits and DI exams stratified by condition, physician specialty, DI modality, and patients' sex and age. Multivariate regressions were used to examine determinants of the outcomes. Results: In FY 2022/2023, 10.4%, 7.0%, and 6.7% of the population saw physicians for shoulder, knee, and LBP conditions, respectively. This costs Alberta $307.04 million ($67.93 per capita), of which shoulder accounted for 41%, knee 28%, and LBP 31%. In the same FY, 17,734 computed tomography (CT), 43,939 magnetic resonance imaging (MRI), 686 ultrasound (US), and 170,936 X-ray exams related to shoulder/knee/LBP conditions were ordered for these patients, costing another $29.07 million, of which CT accounted for 14%, MRI 48%, US 0%, and X-ray 37%. Female, older age, comorbidity scores, and capital zone used physician services more frequently. Patients with a higher comorbidity index scores or more physician visits were more likely being referred for CT or MRI. Conclusion: Musculoskeletal conditions are common and result in patients seeking healthcare services. Visits to family physicians, specialists, and the ordering of DI contribute to extensive utilization of health services, contributing to considerable health system costs.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".