Health care utilization for musculoskeletal disorders
Bibliographic record
Abstract
OBJECTIVE: To examine patterns of ambulatory care and hospital utilization for people with musculoskeletal disorders (MSDs), including arthritis and related conditions, bone and spinal conditions, trauma and related conditions, and unspecified MSDs. METHODS: Administrative data from the Ontario Health Insurance Plan database for ambulatory care physician visits, the National Ambulatory Care Reporting System database for day (outpatient) surgeries and emergency department visits, and the Discharge Abstract Database for hospital discharges were used to examine health care utilization for MSDs in fiscal year 2006-2007. Person visit rates (number of people with physician visits or hospital encounters per population) were calculated. RESULTS: Overall, 22.3% of Ontario's population (2.8 million persons) saw a physician for an MSD in ambulatory settings. Person visit rates were highest for arthritis and related conditions (107.7 per 1,000 population), followed by trauma and related conditions (89.6 per 1,000 population), unspecified MSDs (71.0 per 1,000 population), and bone and spinal conditions (62.4 per 1,000 population). The majority of visits were to primary care physicians, with 83.2% of those with visits for all MSDs seeing a primary care physician at least once. Overall, 33.0% of people with a physician visit for an MSD saw a specialist, with orthopedic surgeons being the most commonly consulted type of specialist. In hospital settings, person visit rates for MSDs were highest in the emergency department, followed by day surgeries and inpatient hospitalizations. CONCLUSION: The findings of our study highlight the magnitude of health care utilization for MSDs and the central role of primary care physicians in the management of these conditions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".