Application of Theory in Chronic Pain Rehabilitation Research and Clinical Practice
Bibliographic record
Abstract
Introduction: Chronic pain has multiple aetiological factors and complexity. Pain theory helps us to guide and organize our thinking to deal with this complexity. The objective of this paper is to critically review the most influential theory in pain science history (the gate control theory of pain) and focus on its implications in chronic pain rehabilitation to minimize disability. Methods: In this narrative review, all the published studies that focused upon pain theory were retrieved from Ovoid Medline (from 1946 till present), EMBAS, AMED and PsycINFO data bases. Results: Chronic pain is considered a disease or dysfunction of the nervous system. In chronic pain conditions, hypersensitivity is thought to develop from changes to the physiological top-down control (inhibitory) mechanism of pain modulation according to the pain theory. Pain hypersensitivity manifestation is considered as abnormal central inhibitory control at the gate controlling mechanism. On the other hand, pain hypersensitivity is a prognostic factor in pain rehabilitation. It is clinically important to detect and manage hypersensitivity responses and their mechanisms. Conclusion: Since somatosensory perception and integration are recognized as a contributor to the pain perception under the theory, then we can use the model to direct interventions aimed at pain relief. The pain theory should be leveraged to develop and refine measurement tools with clinical utility for detecting and monitoring hypersensitivity linked to chronic pain mechanisms.
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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 | 0.086 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.002 | 0.021 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 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".