PATTERNS OF ONSITE MAGNETIC RESONANCE IMAGING EQUIPMENT AMONG ORTHOPEDIC PRACTICES
Bibliographic record
Abstract
BACKGROUND: Despite ongoing policy debate, little is known about the growth in orthopedic surgery practices with onsite magnetic resonance imaging (MRI) capacity, or practice characteristics associated with the acquisition of in-office MRI equipment. METHODS: In July 2012, American Academy of Orthopaedic Surgeons (AAOS) member practices received a web-based survey requesting general information about their practice, such as number practice providers authorized to order MRIs, the type of onsite MRI capacity present (if any), and the date of acquisition for the MRI equipment. Survey responses were augmented with county-level measures of practice area characteristics as of the year of first onsite MRI acquisition (or 2012 for practices without an onsite MRI). RESULTS: The survey obtained usable responses from 740 orthopedic practices, which were geographically representative of AAOS member practices. Forty percent (298) reported onsite MRI capacity. Onsite MRI acquisition occurred at a steady pace over 2000-2012, with no dramatic increase occurring in any particular year over that period. Multivariate logistic regression indicated that practice size (number of providers) was the most important factor affecting the likelihood of onsite MRI acquisition. There was no association between onsite MRI acquisition and any of the county-level practice area characteristics included in the analysis. CONCLUSIONS: Orthopedic practices acquiring onsite MRI equipment on average are much larger than practices without onsite MRI capacity. Larger practices may be more likely to attain the economies of scale necessary to absorb the fixed costs associated with onsite MRI acquisition.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".