Commentary: Multiple Pains as Functional Pain Syndromes
Bibliographic record
Abstract
Pain in various parts of the body is reported by many chil-dren and adolescents, especially girls. Kröner-Herwig, Gassmann, van Gessel, and Vath (2011, this issue) report on a large community study of youth aged 10–17 years in which 66 % of the female respondents and 41 % of the males reported pain in two or more body locations within the last 6 months. Headache, abdominal pain, and back pain were the most frequent of these locations. Kröner-Herwig et al. used multiple regression techniques to identify associations as potential predictors of these pains, finding small contributions of psychosocial variables and large contributions of age, sex, and prior pain. Many interesting questions are raised by the findings of Kröner-Herwig et al., for example: (a) Why do multiple pains often cluster or occur sequentially within individuals as they do? (b) What is the basis of the large sex or gender differences in prevalence of pain? (c) Could multiple pains in adolescence predict later chronic pain and psychological illness? We focus in this commentary on the first of these questions. In addressing this question, we briefly survey adult studies related to various functional pain syndromes, finding that they have many shared features and risk factors. We examine twin studies showing shared ge-netic influences among functional pain syndromes as well as anxiety and depression. We conclude with a por-trayal of functional pain syndromes as largely comprising different manifestations of an underlying propensity or vulnerability to respond to stressors with the experience and report of pain, rather than separate disorders. The main constructs discussed in this commentary are summarized in Figure 1. Why do Multiple Pains often Cluster or Occur Sequentially as they do?
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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 teacher head, 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".