Osteoporosis quality indicators using healthcare utilization data
Bibliographic record
Abstract
SUMMARY: Healthcare utilization data may be used to examine the quality of osteoporosis management by identifying dual-energy X-ray absorptiometry (DXA) testing (sensitivity = 98%, specificity = 93%) and osteoporosis pharmacotherapy (κ = 0.81) with minimal measurement error. INTRODUCTION: In osteoporosis, key quality indicators among older women include risk assessment by DXA and/or pharmacotherapy within 6 months following fracture. METHODS: The purpose of this study was to examine healthcare utilization data for use as quality indicators of osteoporosis management. We linked data from 858 community-dwelling women aged over 65 years who completed a standardized telephone interview about osteoporosis management to their healthcare utilization (medical and pharmacy claims) data. Agreement between self-report of osteoporosis pharmacotherapy and pharmacy claims was examined using kappa statistics. We examined the sensitivity and specificity of medical claims to identify DXA testing as well as the sensitivity and specificity of medical and pharmacy claims to identify those with DXA-documented osteoporosis (T-score ≤ -2.5). RESULTS: Participants were aged 75 (SD = 6) years on average; 96% were Caucasian. Agreement between self-report and claims-based osteoporosis pharmacotherapy was very good (κ = 0.81; 95% CI = 0.76, 0.86). The sensitivity of medical claims to identify DXA testing was 98% (95% CI = 95.9, 99.1), with estimated specificity of 93% (95% CI = 89.8, 95.4). We abstracted DXA results from test reports of 359 women, of whom 114 (32%) were identified with osteoporosis. Medical (osteoporosis diagnosis) and pharmacy (osteoporosis pharmacotherapy) claims within a year after DXA testing had a sensitivity of 80% (95% CI = 71.3, 86.8) and specificity of 72% (95% CI = 66.2, 77.8) to identify DXA-documented osteoporosis. CONCLUSION: Healthcare utilization data may be used to examine the quality of osteoporosis management by identifying DXA testing and osteoporosis pharmacotherapy (care processes) with minimal measurement error. However, medical and pharmacy claims alone do not provide a good means for identifying women with underlying osteoporosis.
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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 | 0.013 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".