Identifying and Addressing Barriers to Osteoporosis Treatment Associated with Improved Outcomes: An Observational Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To identify and address patient-reported barriers in osteoporosis care after a fracture. METHODS: A longitudinal cohort of fragility fracture patients over 50 years of age was seen in a provincewide fracture liaison service. Followup interviews were done at 6 months for osteoporosis care indicators. Univariate statistics were used to describe baseline characteristics, osteoporosis-related outcomes, and reasons cited for not achieving them. Two phases of this program were compared (Phase I: education and communication, and Phase II: risk assessment education and communication). Phase II was further divided into those who fully participated and those who declined. RESULTS: Phase I (n = 3997) had lower testing and treatment rates than Phase II (n = 1363). Rates were highest in those confirmed as having participated in Phase II (n = 569). Phase II nonparticipants (n = 794) had results as in Phase I. In Phase I, the main patient-reported barriers for not visiting their physician or not having a bone mineral density (BMD) test were patient- and physician-oriented (e.g., being instructed by their physician to not have the BMD test). In Phase II, BMD testing was part of the program, thus the main barriers were around treatment choices. Phase II eligible nonparticipants experienced many of the same barriers as Phase I patients, with lower BMD testing rates (54.9% and 65.4%, respectively). CONCLUSION: Evaluating and addressing barriers to guideline implementation reduced those barriers and was associated with higher downstream treatment rates. Monitoring barriers in a program like this provides useful insights for program changes and research interventions.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".