Biomarkers to evaluate distress in cancer patients.
Bibliographic record
Abstract
251 Background: Self-report assessments of stress are a popular, but limiting practice, particularly for underserved populations that may struggle with poor psychological insight and illiteracy. Assessment of stress via biomarkers may be a more inclusive approach to stress assessment for patients with cancer. However, as the rise in popularity regarding biomarker function as outcome data has risen, so to have questions regarding the validity of this endeavor. The current study aimed to explore the convergent validity of inflammatory biomarkers (C-Reactive Protein; CRP, Interleukin 6; IL-6, and Cortisol) while also exploring predictors of stress (e.g., sex, race, marital status) in an underserved, diverse cancer population. Methods: Upon IRB approval, patients ( N = 37) were consented and contributed plasma and serum for the examination of biomarkers (CRP, IL-6, and Cortisol). Next, patients completed the Calgary Symptoms of Stress Inventory (CSOSI; Carlson & Thomas, 2007) to assess for biopsychosocial stress levels. The C-SOSI produces one total score and eight subscales measuring physiological, psychological, and social domains of stress. Results: The mean age of patients was 59.6 ( SD= 13.3) and 56.8% were women. The majority of participants identified as White (56.8%), and a wide variety of cancer types was represented. Biomarkers were analyzed via Quantikine ELISA kits. Correlational analyses revealed that certain subscales showed a positive relationship via medium sized significant correlations. Aside from the Anger subscale, only the physiological subscales were correlated with the biomarkers. The Anger subscale was significantly correlated with Cortisol ( r = .348, p = .035), as was Muscle Tension ( r = .341, p = .039), and Upper Respiratory ( r = .382, p = .02). While Cardiopulmonary Arousal was correlated with CRP ( r = .437, p = .007) and IL-6 ( r = .340, p = .04). Interestingly, IL-6 and CRP were correlated, however, Cortisol was not correlated with IL-6 or CRP. Additional analyses demonstrated significant differences for stress between men and women, which will be discussed. Conclusions: It appears that the biomarkers capture physiological aspects of stress, which may serve in screening patients for psychological intervention.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".