Practice Patterns in Spine Radiograph Utilization Among Doctors of Chiropractic Enrolled in a Provider Network Offering Complementary Care in the United States
Bibliographic record
Abstract
OBJECTIVE: Nonspecific back pain is associated with high use of diagnostic imaging in primary care, yet current evidence suggests that routine imaging of the spine is unnecessary. The objective of this study is to describe current practice patterns in spine radiograph utilization among doctors of chiropractic enrolled in an American provider network. METHODS: A cross-sectional analysis of administrative claims data from one of the largest providers of complementary health care networks for health plans in the United States was performed. Survey data containing provider demographics were linked with routinely collected data on spine radiograph utilization and patient characteristics aggregated at the provider level. We calculated rates and variations of spine radiographs over 12 months. Negative binomial regression was performed to identify significant predictors of high radiograph utilization and to estimate the associated incidence risk ratio. RESULTS: Complete data for 6946 doctors of chiropractic and 249193 adult patients were available for analyses. In 2010, claims were paid for a total of 91542 new patient examinations and 23369 spine radiographs (including 17511 ordered within 5 days of initial patient examination). The rate of spine radiographs within 5 days of an initial patient visit was 204 per 1000 new patient examinations. Significant predictors of higher radiograph utilization rates included the following: practicing in the Midwest or South US census regions, practicing in an urban or suburban setting, chiropractic school attended, and being a male provider in full-time practice with more than 20 years of experience. CONCLUSION: Chiropractic school attended and practice location were the most influential predictors of spine radiograph utilization among network chiropractors. This information may help to inform the development and evaluation of a tailored intervention to address overuse of radiograph utilization.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| 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".