Reinforced pain catastrophizing during menstrual phase among women with primary dysmenorrhea is mediated by cerebral blood flow in the medial prefrontal cortex
Bibliographic record
Abstract
Abstract Pain catastrophizing is a prominent psychological factor that is strongly correlated with pain. Although the complex properties of pain catastrophizing vary across different pain phases, the contribution of chronic pain to its progression from a general trait to a higher state remains unclear. This study aimed to examine the neural mechanisms and degree to which pain catastrophizing is reinforced in the context of primary dysmenorrhea (PDM), one of the most prevalent gynaecological complaints experienced by women of reproductive age. Altogether, 29 women with moderate‐to‐severe PDM were included in this study. Arterial spin labelling was used to quantify the cerebral blood flow (CBF) in each participant in both the pain‐free and painful phases. The pain catastrophizing scale (PCS) was completed in two phases, and the Short‐Form McGill Pain Questionnaire was completed in the painful phase. Compared with pain catastrophizing in the pain‐free phase (PCS pf ), pain catastrophizing in the painful phase (PCS p ) is higher and positively correlated with the composite factor of menstrual pain. CBF analysis indicated that the PCS p is positively associated with CBF in the frontal cortex, hippocampus and amygdala. The reinforcement of pain catastrophizing correlates with CBF in the prefrontal cortex. Specifically, the medial prefrontal cortex, which correlates with pain state, plays a crucial role in mediating the reinforcing effect of pain in the PCS p . These results promote the mechanical comprehension of 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".