Clinical and demographic factors determining patient fracture risk decision point (FRDP): The improving risk communication in osteoporosis (RICO) project
Bibliographic record
Abstract
This study aims to understand how osteoporosis medication acceptance varies across countries with differing guidance on treatment threshold and influence of clinical and demographic factors. A total of 79.2% accepted treatment at a fracture probability at or below the treatment threshold. Fracture history and age did not strongly impact acceptance, suggesting a need for improved fracture risk communication. PURPOSE: This part of the Improving Risk Communication in Osteoporosis (RICO) study aims to understand patients' willingness to initiate osteoporosis treatment given a hypothetical fracture probability-derived from the FRAX® Risk Assessment Tool-and how age, fracture history, and numeric literacy may influence this. METHODS: In 2022-2023, 332 postmenopausal women at risk of fracture were interviewed from nine countries to determine participants' Fracture Risk Decision Point (FRDP), the lowest probability of major osteoporotic fracture at which they would accept an osteoporosis medication. Participants' FRDP was evaluated given eight hypothetical 10-year FRAX scores. RESULTS: In countries with FRAX-based treatment thresholds, over half of the participants per country reported an FRDP that was below the threshold. Collectively, 79.2% demonstrated FRDPs at or below their respective threshold. Age and fracture history did not have a strong influence on FRDP; however, those who demonstrated higher levels of numeric literacy reported a significantly higher median FRDP (10%) compared to those who showed lower levels (5%, p < 0.001). CONCLUSIONS: Most patients were willing to accept an osteoporosis medication prescription at a hypothetical FRAX probability that was even lower than that of their nationally recommended treatment threshold. Literacy scores had a significant influence on FRDP whereas age and fracture history did not.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".