Are Emergency Medicine Provider Characteristics Associated With Diagnostic Imaging for Low Back Pain?
Bibliographic record
Abstract
Background Low back pain (LBP) is one of the most common reasons for presentation to the emergency department (ED). LBP is most commonly non-specific or mechanical in nature yet can be debilitating. Diagnostic imaging (DI) is commonly ordered contrary to guideline recommendations for patients with LBP. This study seeks to determine if physician characteristics are associated with ordering of DI for patients with non-specific or mechanical LBP in the ED. These characteristics include physician sex, age, experience level, location of residency training, and full-time status. Methodology We included all patients presenting to the ED of a Canadian tertiary care center with a diagnosis of non-specific or mechanical LBP between January 2015 and June 2018. We tracked the use of DI for physicians caring for patients presenting to the ED over this period. Simple and multivariable logistic regression analyses were performed, controlling for patient characteristics, to identify provider characteristics that were independently associated with DI use. Results Internationally trained physicians were less likely to order diagnostic radiographs than Canadian trained physicians (odds ratio [OR], 0.625; 95% confidence interval [CI], 0.48-0.95), while middle-aged physicians ordered more computed tomography scans (OR, 6.34; 95% CI, 1.52-26.52) compared to younger physicians; there was no significant difference between younger and older physicians. Conclusions Few physician characteristics were associated with any DI ordering for non-specific or mechanical LBP. The likelihood of receiving DI for non-specific or mechanical LBP may be more strongly related to unmeasured patient characteristics, settings, or logistical factors.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".