Specialty Differences in Initial Evaluation of Patients With Non-Acute Musculoskeletal Pain
Bibliographic record
Abstract
PURPOSE: To explore medical diagnostic testing of new cases of musculoskeletal (MSK) conditions associated with chronic pain. METHODS: We analyzed nationally representative cross-sectional data of people having visits with a new likely chronic MSK pain condition. We documented depression screening and prescribing of diagnostic imaging and blood tests and explored associations between patient and provider factors for each. RESULTS: Over the 9 years of the survey, there were 11,994 initial visits for chronic MSK pain, an average of 36.8 million weighted visits per year or approximately 11.8% of the population. Proportions for depression screening, prescribed imaging, and blood tests were 1.79%, 36.34%, and 9.70%, respectively. People on any public health insurance had twice the increased relative odds to be screened for depression. Orthopedists had 3 times increased relative odds to prescribe imaging compared with family physicians; oncologists had 4 times increased relative odds to prescribe blood tests. Survey year was significantly associated with depression screening and ordering any type of imaging. CONCLUSIONS: Observed rates of depression screening and nonindicated imaging for patients with chronic MSK pain have fluctuated over time. The impact of these fluctuations on clinical practice is as yet unknown. The type of nonrecommended actions varied by specialty of physician.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".