The Impact and Correlation of Anxiety and Depression on Pressure Pain Threshold of Acupoints in Patients with Chronic Pelvic Inflammatory Disease
Bibliographic record
Abstract
Background. Chronic pelvic inflammatory disease (CPID) is a clinically common gynecological disease. Patients experience chronic pelvic pain and often accompany with emotional dysfunction. However, the impact and correlation of anxiety and depression on pain sensitization is not completely known. Objective. To explore the differences and correlations among anxiety, depression, and pressure pain threshold (PPT) of acupoints in patients with CPID. Methods. One hundred and forty-seven patients with CPID were recruited. The Visual Analog Scale (VAS) and short-form McGill Pain Questionnaire (SF-MPQ) were used to assess pain. Self-Rating Anxiety Scale (SAS) and Self-Rating Depression Scale (SDS) were used to evaluate the emotional state of patients. The PPT of acupoints was collected using an electronic Von Frey by two licensed acupuncturists. Results. The CPID patients were divided into anxiety-depression group (group A) or nonanxiety-depression group (group B), according to the SAS and SDS scores. Finally, there were 73 patients in group A and 74 patients in group B. Group A had significantly higher SAS, SDS, VAS, and SF-MPQ scores than group B ( P < 0.05 ). In addition, significant differences were observed in the PPTs of ST28 (R), ST29 (R), SP10 (R), SP9 (R), SP9 (L), ST36 (R), and LR3 (L) between the two groups ( P < 0.05 ). No considerable differences in PPTs at the other acupoints were observed between the two groups. SAS scores showed a positive correlation with PPTs of ST29 (R), SP10 (R), SP9 (L), ST36 (R), and LR3 (L). No remarkable correlation was observed between the SDS scores and PPTs. Conclusion. Anxiety and depression can affect the PPT of some acupoints in CPID patients, which may provide a reference for acupoint selection for acupuncture treatment of CPID with emotional disorders. This trial is registered with ChiCTR2100052632.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".