Musculoskeletal morbidity in adults with spinal cord injuries: A nationwide cohort study
Bibliographic record
Abstract
BACKGROUND: An increase in the demand for quality of life following spinal cord injuries (SCIs) is associated with an increase in musculoskeletal (MSK) pain, highlighting the need for preventive measure research. OBJECTIVE: This study aimed to evaluate the incidence and hazards of MSK morbidities among Korean adults with SCIs, as well as the influence of SCI location on MSK morbidities. METHODS: Patient populations were selected from Korean National Health Insurance Service data (n = 276). The control group included individuals without SCIs (n = 10,000). We compared the incidences and determined the unadjusted and adjusted hazard ratios (HRs) of common MSK morbidities (osteoarthritis, connective tissue disorders, sarcopenia, myalgia, neuralgia, rheumatoid arthritis, myositis, and musculoskeletal infections) based on the location of injury (cervical, thoracic, or lumbar). RESULTS: Adults with SCIs had a higher incidence of MSK morbidity (48.45% vs. 36.6%) and a lower survival probability than those without SCIs. The incidence of MSK morbidity and survival probabilities were not significantly different for cervical cord injuries, whereas both measures were significantly different for thoracic and lumbar injuries. CONCLUSION: SCI increases the risk of MSK morbidity. Lumbar SCI is associated with a higher incidence and risk of MSK morbidity than are cervical or thoracic SCIs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".