Biological research on mental pain, social pain and other pains not primarily felt in the body: methodological systematic review
Bibliographic record
Abstract
BACKGROUND: Researchers explore the biology of painful experiences not primarily felt in the body ('non-physical pain'), sometimes referred to as mental, social or emotional pain. A critical challenge lies in how to operationalise this subjective experience for biological research, a crucial process for translating findings into clinical practice. AIMS: To map studies investigating biological features of non-physical pain, focusing on their conceptual features (i.e. terms and definitions of non-physical pain) and methodological characteristics (e.g. experimental paradigms and measures). METHOD: This methodological systematic review searched reports of primary research on the biological features of non-physical pain across Embase, MEDLINE and Web of Science. Using a meta-research approach, we synthetised results on terms, definitions, populations, experimental paradigms, confounders, measures of non-physical pain and investigation methods (e.g. functional magnetic resonance imaging). RESULTS: We identified 92 human studies, involving 7778 participants. Overall, 59.1% of the studies did not report any definition of non-physical pain, and 82% of studies did not use a specific measure. Regarding the possibility of translating results to clinical settings, most of the human studies involved only healthy participants (71.7%) and the seven different experimental paradigms used to induce non-physical pain had unknown external validity. Confounders were not considered by 32.4% of the experimental studies. Animal studies were rare, with only four rodent studies. CONCLUSIONS: Biomedical studies of non-physical pain use heterogeneous concepts with unclear overlaps and methods with unknown external validity. As has been done for physical pain, priority actions include establishing an agreed definition and measurement of non-physical pain and developing experimental paradigms with good external validity.
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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 | 0.055 | 0.194 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.015 |
| Bibliometrics | 0.021 | 0.020 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".