Non‐Cyclical Mastalgia as a Central Sensitization Component: Implications for Multidisciplinary Treatment Approaches
Bibliographic record
Abstract
OBJECTIVES: Non-Cyclical mastalgia (NCM) is a chronic breast pain condition in women of reproductive age, often linked to anxiety and reduced quality of life (QoL). Evidence suggests central sensitization (CS) may contribute to NCM, but its clinical significance and management remain unclear. METHODS: This cross-sectional study included 201 women aged 25 to 65, with 106 NCM patients and 95 healthy controls, to investigate the association between NCM and central sensitization syndromes (CSS). Data collection involved demographic characteristics and assessments using the Central Sensitization Inventory (CSI), Nottingham Health Profile (NHP), Short-Form McGill Pain Questionnaire (SF-MPQ), Pain Detect, Leeds Assessment of Neuropathic Symptoms and Signs (LANSS), and Hospital Anxiety and Depression Scale (HADS). The main outcome is to reveal the interrelationship between NCM and CSS. RESULTS: NCM patients had significantly higher Central Sensitization Inventory-A (CSI-A) scores than controls (p < 0.001), with 15.1% surpassing the critical threshold of 40, indicating pronounced CS. NCM patients also had increased NHP-1, NHP-2, and HADS-total scores (p = 0.008, p = 0.003, p = 0.004), reflecting greater distress. Subgroup analyses showed more intense pain (p < 0.001) and sleep disturbances (p = 0.002). CSI-A scores strongly correlated with SF-MPQ, LANSS, Pain Detect, and HADS (all p < 0.001). Regression analysis identified pain duration, Pain Detect, and SF-MPQ sensory scores as key predictors of CSI-A (p < 0.001). DISCUSSION: NCM shares characteristics with chronic pain syndromes linked to CSS. Multidisciplinary approaches and innovative treatments, such as cognitive therapies combined with integrative approaches may improve outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".