204. Differences in Pain Experience between Women with and without Osteoporotic Vertebral Fractures
Bibliographic record
Abstract
Background: Osteoporotic vertebral fractures (VFs) are present in ∼12% of older women, but fewer than a third come to clinical attention. One reason for this is lack of clear clinical triggers for referral for radiographs, including a lack of evidence about which characteristics of back pain may indicate the presence of a VF. We have shown that site of back pain can indicate the likelihood of VF, but what is not known is whether the quality or type of back pain in people with VFs is different from those with back pain but no VFs. Methods: A comparative study was undertaken. Digital radiological archives were used to identify a population of potential participants who had a thoracic radiograph for back pain. Inclusion criteria were aged over 60, female and thoracic spinal radiograph performed in the previous 3 months. 683 potential participants were approached, and all who agreed to take part self-completed a questionnaire assembled from domains and scales taken from questionnaires previously validated in UK populations including the McGill Pain Questionnaire and the Keele STarT back pain score as well as demographics. Cases were defined at the end of the study as those with a VF identified from spinal radiographs by the PI using the ABQ method. Chi-squared tests were used to assess univariable associations.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".