Non-pharmacological management of osteoporotic vertebral fractures: Patient perspectives and experiences
Bibliographic record
Abstract
OBJECTIVE: To understand perceptions on rehabilitation after vertebral fracture, non-pharmacological strategies, and virtual care from the perspective of individuals living with vertebral fractures. DESIGN AND SETTING: We conducted semi-structured interviews online and performed a thematic and content analysis from a post-positivism perspective. PARTICIPANTS: Ten individuals living with osteoporotic vertebral fractures (9F, 1 M, aged 71 ± 8 years). RESULTS: Five themes emerged: pain is the defining limitation of vertebral fracture recovery; delayed diagnosis impacts recovery trajectory; living with fear; being dissatisfied with fracture management; and "getting back into the game of life" using non-pharmacological strategies. CONCLUSION: Participants reported back pain and an inability to perform activities of daily living, affecting psychological and social well-being. Physiotherapy, education, and exercise were considered helpful and important to patients; however, issues with fracture identification and referral limited the use of these options. Participants believed that virtual rehabilitation was a feasible and effective alternative to in-person care, but perceived experience with technology, cost, and individualization of programs as barriers.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".