Psychological, Neuropsychological, and Medical Considerations in Assessment and Management of Pain
Bibliographic record
Abstract
Pain is a common yet challenging problem, particularly following traumatic injuries to the head or neck. It is a complex, multidimensional subjective experience with no clear or objective measures; yet it can have a significantly disabling effect across a wide range of functions. Persisting misconceptions owing to mind-body dualism have hampered advances in its understanding and treatment. In this article, a conceptualization of pain informed by recent research and derived from a more useful biopsychosocial model guides discussion of relevant medical, psychological, and neuropsychological considerations. This pain process model explains chronicity in terms of hyperresponsiveness and dysregulation of inhibitory or excitatory pain modulation mechanisms. Related neurocognitive effects of chronic pain are examined and recommendations for minimizing its confounding effects in neuropsychological evaluations are offered. A biopsychosocial assessment model is presented to guide understanding of the myriad of factors that contribute to chronicity. A brief survey of general classes and samples of the more useful pain assessment instruments is included. Finally, this model offers a rational means of organizing and planning individually tailored pain interventions, and some of the most useful pharmacologic, physical, and behavioral strategies are reviewed.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".