Challenges in Defining Quality of Care for Glucocorticoid-induced Osteoporosis: Defending Good Against Perfect
Bibliographic record
Abstract
In an era of increasing preventive care complexity coupled with progressively shorter doctor visits, asymptomatic conditions such as osteoporosis (OP) can easily be neglected. Despite a reported reduction in fracture burden in the United States and Canada1,2, OP continues to be a condition that is both underdiagnosed and undertreated. In the United States, the Healthcare Effectiveness Data and Information Set statistics from the National Committee on Quality Assurance estimates that less than 25% of persons who incur fracture receive a bone-specific medication; a proportion that has remained essentially stagnant over the past 10 years. These US findings are quite similar to the low rates of OP treatment in Canada, as highlighted in the paper in this issue of The Journal by Majumdar and colleagues3. Glucocorticoids constitute the most commonly administered drugs that are associated with bone loss and higher fracture risk. Despite a clear time- and dose-dependent association of glucocorticoids with fracture risk4,5 and international guidelines that support a variety of pharmacotherapies for both primary and secondary prevention6,7,8,9, the prevention and treatment statistics are also rather dismal for glucocorticoid-induced OP (GIOP). Curtis and colleagues observed temporal improvement in the rates of treatment with nonestrogen bone-acting agents among chronic glucocorticoid users of an Aetna managed-care plan over a 5- to 6-year period10. However, even among postmenopausal women, the group at highest fracture risk, less than two-thirds were being treated in the early 2000s. Majumdar and colleagues, using much newer data from Canada, reported that only 25% of over 15,000 older adults receiving supraphysiologic prednisone were managed in accord with the latest guidelines. Thus, an underuse gap persists for GIOP prevention and treatment. Underuse of many evidence-based therapeutics for chronic conditions such as … Address correspondence to Dr. Saag, 1720 2nd Avenue South, Birmingham, AL 35294, USA. E-mail: Ksaag{at}uab.edu
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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 | 0.432 | 0.536 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.005 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.019 | 0.077 |
| Scholarly communication | 0.056 | 0.072 |
| Open science | 0.023 | 0.063 |
| Research integrity | 0.031 | 0.077 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".