Integration of Pain Theories to Guide Knee Osteoarthritis Care
Bibliographic record
Abstract
Osteoarthritis is one of the four leading causes of pain. To date, clinicians providing health care to people with knee osteoarthritis pain focus on evaluating pain intensity and its effect on physical function and provide management with foundations in theories of pain including gate control and specificity. Pain theories such as these have been driving pain management and pain research since the seventeenth century, when Rene Descartes proposed his reflex theory of pain. The purpose of this paper is to describe the evolution of pain theories leading up to the gate control theory and the neuromatrix theory, provide a critical review of these two theories specifically, and discuss the strengths and challenges of integrating these two theories in the guidance of knee osteoarthritis pain management. Integration of the gate control theory, which focuses on the spinal processing of pain, and the neuromatrix theory, which focuses on central processing of pain, gives a broader model for understanding and addressing the multiple dimensions of pain phenomena. The integrated gate control−neuromatrix model presented in this paper provides a theoretical basis for considering the cognitive and affective aspects in addition to the sensory aspects of osteoarthritis pain. Discussion of the multidimensional aspects of pain includes clinical implications and recommendations for evaluation and treatment approaches. Finally, future directions for research are recommended to test the proposed model and improve the management of osteoarthritis pain.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".