Predictive Relationships Between Death Anxiety and Fear of Cancer Recurrence in Patients with Breast Cancer: A Cross-Lagged Panel Network Analysis
Bibliographic record
Abstract
The aim of this study was to explore the longitudinal relationship between death anxiety (DA) and fear of cancer recurrence (FCR) in women newly diagnosed with breast cancer at baseline and 3 months post-discharge. A total of 426 women with breast cancer completed the Templer's Death Anxiety Scale and the Fear of Cancer Recurrence Inventory at hospital discharge and 3 months later. Cross-lagged panel analysis (CLPA) was used to describe the relationship of the two variables (DA and FCR) over time and identify the optimal intervention symptom nodes for breast cancer patients in different stages. The findings suggest that the specific symptoms of DA, known as "cognition", predict the subsequent symptom development for a variety of mental health problems in the network structure. The "Psychological distress" symptom in FCR is the most susceptible to other symptoms. In addition, death-related cognition may be a bridge symptom that connects the co-occurrence of DA and FCR. Death-related "time awareness" is the optimal symptom node for intervention in early-stage breast cancer patients, while it is "cognition" in advanced patients. The death-related cognition and emotional regulation of death may be the best target for interventions among breast cancer patients, considering their DA coincides with FCR. The best intervention for patients with early-stage breast cancer may be the time awareness of death, while it may be more effective for patients with advanced cancer to be educated about disease and death, as well as to enhance correct perception.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".