Do patients perceive a link between a fragility fracture and osteoporosis?
Bibliographic record
Abstract
BACKGROUND: To evaluate factors associated with whether patients associate their fracture with future fracture risk. METHODS: Fragility fracture patients participated in a telephone interview. Unadjusted odds ratios (OR, [95% CI]) were calculated to identify factors associated with whether patients associate their fracture with increased fracture risk or osteoporosis. Predictors identified in univariate analysis were entered into multivariable logistic regression models. RESULTS: 127 fragility fracture patients (82% female) participated in the study, mean (SD) age 67.5 (12.7) years. An osteoporosis diagnosis was reported in 56 (44%) participants, but only 17% thought their fracture was related to osteoporosis. Less than 50% perceived themselves at increased risk of fracture. The odds of an individual perceiving themselves at increased risk for fracture were higher for those that reported a diagnosis of osteoporosis (OR 22.91 [95%CI 7.45;70.44], p < 0.001), but the odds decreased with increasing age (0.95 [0.91;0.99], p<0.009). The only variable significantly associated with the perception that the fracture was related to osteoporosis was self-reported osteoporosis diagnosis (39.83 [8.15;194.71], p<0.001). CONCLUSION: Many fragility fracture patients do not associate their fracture with osteoporosis. It is crucial for physicians to communicate to patients that an osteoporosis diagnosis, increasing age or a fragility fracture increases the risk for future 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".