Modulation effect of acupuncture treatment on chronic neck and shoulder pain in female patients: Evidence from periaqueductal gray‐based functional connectivity
Bibliographic record
Abstract
AIMS: Chronic neck and shoulder pain (CNSP) is a common neurological disorder, which females are more likely to suffer from. The periaqueductal gray (PAG) plays a key role in the descending modulation of pain. This study aimed to investigate altered PAG-based functional connectivity (FC) in female patients with CNSP related to healthy controls (HCs) and the effect of acupuncture for female patients with CNSP using PAG-based FC biomarkers. METHODS: PAG-based FC value was calculated based on resting-state functional images and then compared between patients with CNSP at pre-acupuncture, post-acupuncture, and HCs. Then, correlational analyses were performed to examine the relationships between increased PAG-based FC strength and improved clinical parameters in patients after acupuncture treatment. RESULTS: Before acupuncture treatment, compared to HCs, patients with CSNP showed altered PAG-based FC with widely distributed brain regions, including the left medial superior frontal gyrus, bilateral posterior insula (pIns), and cingulate gyrus. After treatment, patients with CNSP exhibited specially improved PAG-pIns FC compared to that before treatment, and no significant difference was observed in the increased PAG-pIns FC strength between HCs and patients with CNSP after treatment. Furthermore, pain catastrophizing reduction was significantly correlated with the increased PAG-pIns FC strength in patients after treatment. CONCLUSION: The effect of acupuncture treatment may relate to the increased PAG-pIns FC, which significantly correlated with pain catastrophizing reduction after treatment. These findings shed important mechanistic information on the role of therapeutic approaches in treating chronic neck and shoulder 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) | 0.002 | 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".