Provider characteristics: impact on bone density utilization at a health maintenance organization
Bibliographic record
Abstract
OBJECTIVE: Information from bone mineral density (BMD) is relevant in guiding postmenopausal osteoporosis prevention and treatment, yet many providers do not typically order BMDs. This study was designed to identify physician characteristics associated with utilization of bone densitometry in a northeastern US health maintenance organization (HMO). DESIGN: Internal medicine primary providers in practice at a northeastern HMO between April 1997 and March 1998 were categorized by BMD utilization, based on the number of BMDs performed during that time per number of women older than 50 years in their patient panel. In one analysis, providers in the highest quintile for this parameter were considered "high utilizers" (n = 25), and those in the lowest quintile, "low utilizers." These groups were compared with respect to provider characteristics and practice composition. In a second analysis, multiple variable linear regression was used to predict the likelihood of utilization of BMD as a function of those parameters for all providers. RESULTS: The range of BMDs by provider was 0 to 190 per 1,000 women >50 years (median = 34) over this 1-year period. Providers who were high utilizers had a significantly greater number of female patients more than age 50 in their practice (p < 0.05) and were also more likely themselves to be female (62% vs. 48%; p < 0.05). There was no association between BMD utilization and age of provider or years in practice. Female provider gender (p < 0.01) and greater percentage of women more than age 50 in the practice (p < 0.05) were independent predictors of BMD utilization in a multivariate model. CONCLUSION: BMDs were infrequently utilized by the majority of providers over this 1-year period. Female provider gender was associated with a significantly greater likelihood of BMD utilization that was not simply explained by the greater number of women in these providers' practices. These findings may be relevant to identifying strategies to improve bone health care in this population.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.004 | 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".