The Biological Psychology of Pain
Bibliographic record
Abstract
Abstract The chapter provides a comprehensive review of the current knowledge concerning the anatomical, physiological, and neurochemical substrates that underlie both physiological and pathological pain. Thus, we describe in detail the pathways that underlie the transmission of inputs from the periphery to the CNS, the physiological properties of the neurons activated by painful stimuli, and the neurochemicals that mediate or modulate synaptic transmission in somatosensory pathways. A special effort has been made to identify critical advances in the field of pain research that point to the processes by which pathological pain develops following tissue or nerve injury, as well as how pain is modulated by various brain mechanisms. The review of the peripheral nervous system concentrates on differences in the properties of the primary afferent neurons that transduce and relay painful messages to the CNS, and how their responses are affected by injuries that produce inflammation. The review of spinal cord dorsal horn concentrates on what is known about the neurochemical influences on synaptic transmission, and how they are affected by inflammatory and nerve injuries in the periphery. The review of pain processing in the brain focuses on the role of various brain regions in the determination of the multidimensional nature of pain, as discerned by anatomical connectivity, physiological function, and brain imaging. Finally, insights are provided into the future of pain research, with a focus on molecular biology, and behavioral genetics as a means to understand individual differences in pain sensitivity and expression.
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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 | 0.000 | 0.001 |
| 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 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".