Preventing the next fragility fracture: a cross-sectional survey of secondary fragility fracture prevention services worldwide
Bibliographic record
Abstract
BACKGROUND: There has been an increasing awareness of the public health impact of fragility fractures due to osteoporosis and the imperative of addressing this health burden with well-designed secondary fragility fracture prevention services (SFFPS). The objectives of this survey, conducted within the international membership of the Fragility Fracture Network (FFN), were to identify gaps in services and identify the needs for further training and mentorship to improve the quality of SFFPS provided to patients who sustain fragility fractures. METHODS: We conducted an electronic cross-sectional survey of FFN Secondary Fracture Prevention Special Interest Group (SIG) members from April 2021 to June 2021 using SurveyMonkey. The survey questions were developed by four SIG members from New Zealand, Australia, Canada and the Netherlands, who have experience in developing, implementing and evaluating SFFPS. The sampling framework was convenience sampling of all 1162 registered FFN Secondary Fracture Prevention SIG members. Descriptive analyses were performed for all variables and presented as frequencies and percentages. RESULTS: 69 individuals participated in the survey, from 34 different countries over six continents, with a response rate of 6% (69/1162). Almost one-third of respondents (22/69) were from 15 countries within the European continent. Key findings included: (1) 25% of SFFPS only included patients with hip fracture; (2) less than 5% of SFFPS had any mandatory core competencies for training; (3) 38.7% of SFFPS were required to collect key performance indicators; and (4) 9% were collecting patient-reported outcome measures. CONCLUSIONS: This survey identified key areas for improving SFFPS, including: expanding the reach of SFFPS to more patients with fragility fracture, developing international core competencies for health provider training, using key performance indicators to improve SFFPS and including the patient voice in SFFPS development. These findings will be used by the FFN to support SFFPS development internationally.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".