Osteoporosis Knowledge Among Individuals With Recent Fragility Fracture
Bibliographic record
Abstract
BACKGROUND: To evaluate osteoporosis knowledge among patients with fractures and to evaluate factors associated with osteoporosis knowledge. METHODS: Patients with fragility fractures participated in a telephone interview. Participants were asked what they thought osteoporosis was. Unadjusted odds ratios (OR, 95% CI) were calculated to identify factors associated with a correct definition. Predictors identified in univariate analysis were entered into multivariable logistic regression models. A subset also completed the Facts on Osteoporosis Quiz. RESULTS: One hundred twenty-seven patients (82% women) participated in the study, with mean (SD) age being 67.5 (12.7) years. Ninety-five (75%) respondents gave correct osteoporosis definitions. The odds of an individual providing a correct definition of osteoporosis were higher for those who reported a diagnosis of osteoporosis or those who reported higher education levels, but the odds decreased with increasing age. A total of 49 (39%) respondents completed the Facts on Osteoporosis Quiz; the average score was 13.6 (3.8) of 21. Areas that respondents scored poorly on were related to key risk factors. CONCLUSION: Many patients with fractures are unaware of important risk factors. Education initiatives aimed at improving osteoporosis knowledge should be directed at individuals at high risk of fracture. Nurses and other allied healthcare providers working in fracture clinics, acute care, and rehabilitation settings are in an ideal position to communicate information about osteoporosis and fracture risk to individuals with a recent fragility fracture.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".