Factors associated with physician prescribing behavior of dipeptidyl peptidase-4 inhibitors for type 2 diabetes in the US outpatient population
Bibliographic record
Abstract
Objective: Although the use of dipeptidyl peptidase-4 (DPP-4) inhibitors has been increasing after their first approval in 2006, little is known about their prescribing pattern. Therefore, the objective of this study is to evaluate the prescribing pattern of the DPP-4 inhibitors for the treatment of type 2 diabetes mellitus (T2DM) and examine sociological factors associated with physician prescribing behavior in the U.S. outpatient setting.Methods: This cross-sectional study was conducted utilizing data from the 2006-2010 National Ambulatory Medical Care Survey (NAMCS) and employed the Eisenberg model that explains physician decision making in the context of sociologic influences. For independent variables, the following characteristics were determined based on the Eisenberg model: patient characteristics, physician characteristics, the physician-health care system interaction, and the physician-patient relationship. The dependent variable was the use of DPP-4 inhibitors. Multivariate logistic regressions were used for analyses.Results: The estimated population size was 535,158,796 patients during five years, and 3.85% of them were prescribed DPP-4 inhibitors. Among the patient characteristic-related factors, the odds of the use of DPP-4 inhibitors was 73% lower in patients with Medicaid compared to patients with private insurance (OR = 0.27; 95% CI, 0.08-0.88; p = .030). For the physician characteristic-related factor, the odds of prescribing DPP-4 inhibitors for primary care physicians are about 86% higher than the odds for non-primary care physicians (OR = 1.86; 95% CI, 1.17-2.95; p = .008). In addition, physicians in private offices were 3.01 times more likely to prescribe DPP-4 inhibitors than physicians in the health maintenance organizations (HMO) (OR = 3.01; 95% CI, 1.03-8.78; p = .043).Conclusions: Patient characteristics, physician characteristics, and the physician’s relationship with the health care system were associated with an increased use of DPP-4 inhibitors. However, the physician’s relationship with the patient was not associated with an increased use of DPP-4 inhibitors.
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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 | 0.005 |
| 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".