Spinal manipulative therapy and its role in the prevention, treatment and management of chronic pain.
Bibliographic record
Abstract
Chronic pain is a worldwide epidemic. It is characterized as “pain that persists beyond normal tissue healing time”1 and is physiologically distinct from acute nociceptive pain. The current research estimates the prevalence of chronic pain in the general population to be anywhere from 10–55%,2 predominantly affecting the adult population. Studies indicate that the prevalence of chronic pain in the over-60 age group is double that for younger adults.3 Furthermore, over 80% of elderly (over 65) adults suffer from some form of painful chronic joint disease4 and greater than 85% of the general population will experience some form of chronic myofascial pain during their lifetime.5 Chronic pain has substantial impact on sufferers, often citing significant impairments in physical, social and psychological function.6 Many patients suffer from progressive health and physical deterioration owing to sleep and appetite disturbances, anxiety, depression, decreased physical energy and activity as well as excessive use of medication.6 Chronic pain often leads to social withdrawal, impaired personal relationships and job loss.1 Recent estimates suggest that 50–85% of adults report some degree of pain that may interfere with daily activities and quality of life.7 Chronic pain sufferers are five times more likely to utilize health care services than non-pain sufferers.8 Conservative figures estimate that the annual cost of managing chronic pain in the United States currently exceeds $40 billion annually.9 Of greatest concern is the fact that the ratio of the over-65:under-65 segments of the population is projected to double by 2050,10 promising to make chronic pain one of healthcare’s foremost challenges in the future.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".