Bridging the Osteoporosis Quality Chasm
Bibliographic record
Abstract
The persistent chasm between best evidence and best practices in osteoporosis is an international phenomenon. There is considerable evidence that individuals in many developed nations who experience a fragility fracture are not receiving adequate osteoporosis management. Among these persons, many go on to experience subsequent fractures, and many have never been told they may have osteoporosis, let alone been tested or treated. Efforts to improve quality of care in osteoporosis are predicated on first defining the term “quality.” In the United States and internationally, many groups have established quality metrics, often referred to as performance measures or quality indicators. When properly constructed, these metrics represent minimal acceptable standards of care that can be used by physicians and health plans to establish and monitor quality. Most performance measures focus on the process of care. Process measures are used preferentially because they are evidence based, actionable, measurable, and do not need to account for compliance and other patient characteristics that may influence actual health outcomes. As shown in Table 1, performance measures contain a denominator expressing the at risk population (i.e., number of women over the age of 65) and a numerator (i.e., the number of women who have received either a BMD test or an anti‐osteoporotic therapy). In the United States, the National Committee on Quality Assurance (NCQA), which devises the Healthcare Effectiveness Data and Information Set (HEDIS) quality measures,(1) first implemented an osteoporosis performance measure in 2003. The HEDIS measures assess the performance of the majority of U.S. health plans. The osteoporosis HEDIS measure defines the proportion of women ≥67 yr of age with a new fracture who received either a BMD test or prescription treatment for osteoporosis within 6 mo of their fracture. Other groups including the American Medical Association Physician Consortium for Performance Improvement and the Joint Commissions also have established osteoporosis performance measures. These added measures include evaluation for secondary osteoporosis, education regarding calcium and vitamin D supplementation, physical activity and fall risk assessment, continuity of care, monitoring, and recommendations on appropriate timing of pharmacotherapy.(1,2)
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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 | 0.120 | 0.226 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.025 | 0.023 |
| Open science | 0.005 | 0.037 |
| Research integrity | 0.014 | 0.025 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".