Secondary Impairments After Spinal Cord Injury
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence of secondary impairments among individuals with long-standing spinal cord injury in Quebec and the potential relationships between these impairments and several variables. DESIGN: A review of 2,200 medical files was conducted to determine the target population; 976 patients were selected randomly and mailed questionnaires. The results were based on 482 individuals with spinal cord injury who returned the completed questionnaire. The questionnaire included 14 subsections, such as sociodemographic, medical, psychosocial, and environmental information. The medical section, including the type and level of lesion and the presence of secondary impairments, was analyzed. RESULTS: Urinary tract infection, spasticity, and hypotension were the most frequently reported secondary impairments, regardless of the severity of injury. Relationships between the prevalence of secondary impairments and the duration of injury, as well as perceived health status, were observed. CONCLUSIONS: This is the first study to describe secondary impairments after long-standing spinal cord injury in Quebec. Patients with spinal cord injury still present a high prevalence of secondary impairments many years after their rehabilitation, despite preventive education or medical follow-up visits. Further studies are required to determine the specific impact that these impairments have on the patients' social role and their quality-of-life.
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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 | 0.001 | 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) | 0.004 | 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".