Unexplained and Underpowered: The Relationship Between Psychosomatic Disorders and Interpersonal Abuse—A Critical Review
Bibliographic record
Abstract
Although it is commonly accepted that interpersonal violence (IntPV) leads to adverse health consequences, the available data are far from decisive. To test the hypothesized link, the authors devised an evidence-based strategy to determine the data quality in studies purporting to link IntPV and some medically unexplained disorders in women (irritable bowel syndrome, chronic pelvic pain, fibromyalgia/chronic fatigue, and other chronic pain syndromes). English language studies with control groups of unaffected women were assessed for the quality of their methodologies. The number of studies, together with the consistency of their findings in each domain, was collated to determine the overall weight of evidence regarding the link for each condition. The quantity and quality of research in each clinical area proved to be sparse. In general, most research was limited to small, convenience samples, with insufficient attention to the design of control groups and to sample size. The evidence currently available regarding irritable bowel syndrome, fibromyalgia/chronic fatigue, chronic pelvic pain, and other chronic pain syndromes does not allow for any firm conclusion regarding their link to IntPV. More research - paying particular regard to the methodological concerns identified here - is required in order to generate any definitive conclusions.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 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".