The reinforced pain catastrophizing and its neural association with chronic menstrual pain
Bibliographic record
Abstract
Pain catastrophizing is a prominent psychological factor that exhibits a robust correlation with pain. Although the complex property of pain catastrophizing was reported to vary across different pain phases, the contribution of chronic pain in its progression from general trait to a higher state remains unclear. The objective of this study is to examine the neural mechanism and degree to which pain catastrophizing is reinforced in the context of primary dysmenorrhea (PDM), which is one of the most prevalent gynecological complaints experienced by reproductive women. 29 women with moderate to severe PDM were included in this study. Arterial spin labeling was utilized to quantify the cerebral blood flow (CBF) for each subject in both pain-free and painful phases. The pain catastrophizing scale (PCS) was completed in two phases and the Short-Formed McGill Pain Questionnaire was completed in the painful phase. Compared to the pain catastrophizing in pain-free phase (PCS pf ), the pain catastrophizing in the painful phase (PCS p ) is higher and positively correlated with the compositive factor of menstrual pain. CBF analysis indicates the PCS p is positively associated with the CBF of frontal cortex, hippocampus and amygdala. The reinforcement of pain catastrophizing is correlated with the CBF of prefrontal cortex. Specifically, the medial prefrontal cortex, which correlates with pain state as well, plays a crucial role in mediating the reinforcing effect of pain on PCS p . These results promote both the phenomenological and mechanical comprehension regarding pain catastrophizing management in individuals with chronic pain.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".