Clusters of Psychological Symptoms in Breast Cancer
Bibliographic record
Abstract
BACKGROUND: Breast cancer patients tend to experience numerous concurrent psychological symptoms that form clusters. It has been proposed that a common psychological mechanism may underlie the membership of symptoms in a given cluster, but this hypothesis has never been investigated. Maladaptive emotion regulation (ER) is one possible common mechanism. OBJECTIVE: This study examined cross-sectional and prospective relationships between subjective (experiential avoidance, expressive suppression, and cognitive reappraisal) and objective (high-frequency heart rate variability) measures of ER and clusters of psychological symptoms among women receiving radiation therapy for breast cancer. METHOD: A total of 81 women completed a battery of self-report scales before (T1) and after (T2) radiotherapy, including measures of anxiety, depression, fear of cancer recurrence, insomnia, fatigue, pain, and cognitive impairments. Resting high-frequency heart rate variability was measured at T1. RESULTS: Latent profile analyses identified between 2 and 3 clusters of patients with similar levels of symptoms at T1 and T2 and with a similar profile of symptom changes between T1 and T2. Discriminant analyses showed that higher levels of avoidance and suppression predicted membership in symptom clusters that included more severe symptoms cross-sectionally at T1 and at T2 (both P values < .0001). However, ER at T1 did not significantly predict membership in clusters of symptom changes between T1 and T2 (P = .15). CONCLUSION: Maladaptive ER strategies, more particularly suppression and avoidance, are a possible psychological mechanism underlying clusters of cancer-related psychological symptoms. IMPLICATIONS FOR PRACTICE: Psychological interventions targeting maladaptive ER strategies have the potential to treat several psychological symptoms simultaneously.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".