The experience of low back pain in people with incomplete spinal cord injury in the USA, UK and Greece
Bibliographic record
Abstract
Aims/Background: To investigate the prevalence of low back pain in people with incomplete spinal cord injury and compare these characteristics among three countries. Methods: A cross-sectional, primarily internet based survey, was conducted in the USA, UK and Greece. The Short Form McGill Pain Questionnaire was the main measure used. In addition, data were collected on the presence, onset, duration and frequency of low back pain. Findings: A total of 219 questionnaires were included in the analysis. Anytime low back pain was 74% (95% confidence interval [CI] 67, 79) and current low back pain was 66% (95% CI 59, 72). People with paraplegia were 2.75 times more likely to report low back pain anytime post incomplete spinal cord injury than people with tetraplegia (95% CI 1.38, 5.47). Thirty-three percent of participants reported low back pain onset immediately post incomplete spinal cord injury and 44% reported daily low back pain with people from UK reporting the highest percentage (59%). The more low back pain days felt in a month the worse its quality and intensity. Low back pain is described as ‘discomforting’ with moderate intensity and people from the UK reported the worst low back pain. Finally, people from Greece reported better results for the sensory component of their low back pain. Conclusions: Despite some differences in profile and injury characteristics of the groups from the three nations, low back pain presence in incomplete spinal cord injury is reported highly for all people in the countries investigated.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".