Medically Unexplained Pain Is Not Caused by Psychopathology
Bibliographic record
Abstract
This paper highlights Professor Ronald Melzack′s theoretical contributions to the understanding of psychological factors in pain. His work continues to have profound influences on pain theory, research, management and public policy. His ideas have been pivotal to the acceptance of the role of the brain and psychological factors in the experience of pain. This article briefly outlines the prevailing theories of the psychology of pain before the gate‐control theory. Melzack′s contributions argue against the simplistic linear thinking inherent in specificity theory, which leads to the attribution of pain to either ′organic′ or ′psychogenic′ causes. Nevertheless, Cartesian dualism continues to thrive. The authors illustrate the nature and extent to which dualistic thinking pervades the field, show that a dualistic conceptualization of pain introduces an element of distrust to the relationship between patient and health professional, and conclude that the available data fail to reach today′s standards for an evidence‐based approach to pain. The authors believe that medically unexplained pain is not a symptom of a psychological disorder and that it is time to abandon the thinking that separates mind and body. The challenge remains for proponents to provide the empirical evidence to prove that psychopathology causes pain and, in so doing, to specify the mechanisms by which it is generated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".