Acceptability and Feasibility of an Evidence-Based Requisition for Bone Mineral Density Testing in Clinical Practice
Bibliographic record
Abstract
Introduction.The purpose of this study is to understand the experience of primary care providers (PCPs) using an evidence-based requisition for bone mineral density (BMD) testing.Methods.A qualitative descriptive approach was adopted. Participants were given 3 BMD Recommended Use Requisitions (RUR) to use over a 2-month period. Twenty-six PCPs were interviewed before using the RUR. Those who had received at least one BMD report resulting from RUR use were then interviewed again. An inductive thematic analysis was performed.Results.We identified four themes in interview data: (1) positive and negative characteristics of the RUR, (2) facilitators and barriers for implementation, (3) impact of the RUR, and (4) requisition preference. Positive characteristics of the RUR related to both its content and format. Negative characteristics related to the increased amount of time needed to complete the form. Facilitators to implementation included electronic availability and organizational endorsement. Time constraints were identified as a barrier to implementation. Participants perceived that the RUR would promote appropriate referrals and the majority of participants preferred the RUR to their current requisition.Conclusions.Findings from this study provide support for the RUR as an acceptable point-of-care tool for PCPs to promote appropriate BMD testing.
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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 | 0.100 | 0.243 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".