Bibliographic record
Abstract
Research has focused on pain perception of individuals with Borderline Personality Disorder, but there is a lack of research regarding pain perception for other types of Personality Disorders. The present study explored associations between the perception of experimentally-induced acute pain of individuals without acute or chronic pain with Borderline, as well as Histrionic, and Schizotypal, Personality Disorders traits. The primary question of interest was whether any personality disorders were associated with altered pain perception. Fifty-two participants had pain induced by a cold-pressor task, and were evaluated for personality disorder traits. Psychophysiological perception of pain was measured using pain threshold and tolerance tests, subjective reports of pain were taken using the McGill Pain Questionnaire, and physiological aspects were measured using Galvanic Skin Response as an index of physiological arousal. The results showed significant associations between Histrionic Personality Disorder and subjective reports of the sensory aspects, and intensity, of pain, but not with psychophysiological or physiological responses (although caution is needed in interpreting the results of multiple tests). There were no significant associations regarding pain perception and Borderline, or Schizotypal, Personality Disorders. These results are preliminary, but provide novel suggestions regarding the impact of Personality Disorder on pain perception and guidance for future research has been provided.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".