Access to Osteoporosis Treatment is Critically Linked to Access to Dual-Energy X-ray Absorptiometry Testing
Bibliographic record
Abstract
OBJECTIVE: To determine if inequities in access to osteoporosis investigation [dual-energy x-ray absorptiometry (DXA) testing] and treatment (bisphosphonate, calcitonin, and/or raloxifene) exist among older women in a region with universal health care coverage. METHODS: Community-dwelling women aged 65-89 years residing within 2 regions of Ontario, Canada were randomly sampled. Data were collected by standardized telephone interview. Potential correlates of DXA testing (verified by physician records), and current treatment were grouped by type as: "predisposing characteristics," "enabling resources," or "need factors" based on hypothesized relationships formulated before data collection. Variables associated with each outcome independent of "need factors" identified inequities in the system. RESULTS: Of the 871 participants (72% response rate), 55% had been tested by DXA and 20% were receiving treatment. Using multiple variable logistic regression to adjust for need factors, significant inequities in access to DXA testing existed by age, health beliefs, education, income, use of preventive health services, region, and provider sex. DXA testing mediated access to treatment; 34% of those having had a DXA were treated compared with 2% of those who did not. Among women with osteoporosis, correctly reporting that their DXA test indicated osteoporosis and higher perceived benefits of taking pharmacological agents for osteoporosis were associated with treatment. CONCLUSIONS: Significant inequities in access to fracture prevention exist in a region with universal health care coverage. Improved access to DXA and better communication to patients of both their DXA results and the benefits of treatment has the potential to reduce the burden of osteoporosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".