Alleviating Excessive Worries Improves Co-Occurring Depression and Pain in Adolescent and Young Adult Cancer Patients: A Network Approach
Bibliographic record
Abstract
Objective: Anxiety, depression, and pain are highly interactive with each other in adolescent and young adult (AYA) cancer patients. This study aims to map out the connectivity between anxiety, depression and pain symptoms amongst Chinese AYA cancer patients from the perspective of a network model. Methods: Two hundred and eighteen AYA patients, aged between 15 and 39 years at diagnosis; completed the Patient Health Questionnaire (PHQ), Generalized Anxiety Disorder (GAD), and McGill Pain Questionnaire-Visual Analogue Scale (MPQ-VAS). Network analyses were performed. Results: In all, 38.07% (95% CI = 31.58-44.57%) of the participants reported depression, 30.73% (95% CI = 24.56-36.91%) reported anxiety, and 14.22% (95% CI = 9.55-18.89%) reported current pain. The generated network illustrated that anxiety, depression and pain community were well connected. In the network, "having trouble relaxing" (GAD4, node strength = 1.182), "uncontrollable worry" (GAD2, node strength = 1.165), and "sad mood" (PHQ2, node strength = 1.144) were identified as the most central symptoms, while "uncontrollable worry" (GAD2, bridge strength = 0.645), "guilty" (PHQ6, bridge strength = 0.545), and "restlessness" (GAD5, bridge strength = 0.414) were the key bridging symptoms that connected different communities. Conclusion: Anxiety, depression and pain symptoms are highly interactive with each other. Alleviating AYA cancer patient's excessive worries might be helpful in improving the patient's co-occurring anxiety, depression and pain symptoms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".