Prevalence and characteristics of chronic pain in veterans with spinal cord injury
Bibliographic record
Abstract
To assess prevalence and characteristics of individual chronic (>6 mo) pain components in the veteran spinal cord injury (SCI) population, we conducted a telephone survey with 348 (66%) of 530 veterans with SCI who received care from one regional Department of Veterans Affairs SCI center during a 3 yr period. The short-form McGill Pain Questionnaire was used to assess qualitative properties of the pain experience. Other questions were used to assess frequency, duration, intensity, exacerbating factors, and effects on daily activities. Of the participants, 75% reported at least one chronic pain component. The majority (83%) of the chronic pain components occurred daily (mean = 27.4 d/mo) and lasted most of the day (mean = 17.4 h/d). Mean pain intensity in the week before the interview averaged 6.7 (on a 0 to 10 scale), while worst pain intensity averaged 8.6. Two-thirds (67%) of the chronic pain components interfered with daily activities. The most commonly selected pain descriptors were "aching," "sharp," "hot-burning," and "tiring-exhausting." More research is needed to identify better ways to prevent, assess, and treat chronic pain in the veteran SCI population.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".