Exploring the Association between Pain and Fracture Characteristics in Women with Osteoporotic Vertebral Fractures
Bibliographic record
Abstract
Purpose: The purpose of this study was to estimate the association between pain and the number, severity, and location of fractures in women with osteoporotic vertebral fractures. Method: We used an 11-point numeric pain rating scale to assess pain during movement in the preceding week and lateral spinal radiographs to confirm number, location, and severity of vertebral fractures. In model 1, we assessed the association between pain during movement and the number, severity, and location of fractures. We adjusted model 2 for pain medication use and age. Results: The mean age of participants was 76.4 (SD 6.9) years. We found no statistically significant associations between pain and fracture number (estimated β = 0.23, 95% CI: –0.27, 0.68), fracture severity (estimated β = –0.46, 95% CI: –1.38, 0.49), or fracture location at T4–T8 (estimated β = 0.06, 95% CI: –1.26, 1.34), T9–L1 (estimated β = 0.35, 95% CI: –1.17, 1.74), or L2–L4 (estimated β = 0.40, 95% CI: –1.01, 1.75). Age and pain medication use were not significantly associated with pain. Model 1 accounted for 4.7% and model 2 for 7.2% of the variance in self-reported pain. Conclusion: The number, location, and severity of fractures do not appear to be the primary explanation for pain in women with vertebral fractures. Clinicians must consider other factors contributing to pain.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".