Using self-reports of pain and other variables to distinguish between older women with back pain due to vertebral fractures and those with back pain due to degenerative changes
Bibliographic record
Abstract
UNLABELLED: Women with back pain and vertebral fractures describe different pain experiences than women without vertebral fractures, particularly a shorter duration of back pain, crushing pain and pain that improves on lying down. This suggests a questionnaire could be developed to identify older women who may have osteoporotic vertebral fractures. INTRODUCTION: Approximately 12 % of postmenopausal women have vertebral fractures (VFs), but less than a third come to clinical attention. Distinguishing back pain likely to relate to VF from other types of back pain may ensure appropriate diagnostic radiographs, leading to treatment initiation. This study investigated whether characteristics of back pain in women with VF are different from those in women with no VFs. METHODS: A case control study was undertaken with women aged ≥60 years who had undergone thoracic spinal radiograph in the previous 3 months. Cases were defined as those with VFs identified using the algorithm-based qualitative (ABQ) method. Six hundred eighty-three potential participants were approached. Data were collected by self-completed questionnaire including the McGill Pain Questionnaire. Chi-squared tests assessed univariable associations; logistic regression identified independent predictors of VFs. Receiver operating characteristic (ROC) curves were used to evaluate the ability of the combined independent predictors to differentiate between women with and without VFs via area under the curve (AUC) statistics. RESULTS: One hundred ninety-seven women participated: 64 cases and 133 controls. Radiographs of controls were more likely to show moderate/severe degenerative change than cases (54.1 vs 29.7 %, P = 0.011). Independent predictors of VF were older age, history of previous fracture, shorter duration of back pain, pain described as crushing, pain improving on lying down and pain not spreading down the legs. AUC for combination of these factors was 0.85 (95 % CI 0.79 to 0.92). CONCLUSION: We present the first evidence that back pain experienced by women with osteoporotic VF is different to back pain related solely to degenerative change.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".