A bio-behavioral model of systemic inflammation at breast cancer diagnosis and fatigue of clinical importance 2 years later
Bibliographic record
Abstract
BACKGROUND: We aimed to generate a model of cancer-related fatigue (CRF) of clinical importance 2 years after diagnosis of breast cancer building on clinical and behavioral factors and integrating pre-treatment markers of systemic inflammation. PATIENTS AND METHODS: Women with stage I-III hormone receptor-positive/human epidermal growth factor receptor 2-negative breast cancer were included from the multimodal, prospective CANTO cohort (NCT01993498). The primary outcome was global CRF of clinical importance [European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire (QLQ)-C30 ≥40/100] 2 years after diagnosis (year 2). Secondary outcomes included physical, emotional, and cognitive CRF (EORTC QLQ-FA12). All pre-treatment candidate variables were assessed at diagnosis, including inflammatory markers [interleukin (IL)-1α, IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10, interferon γ, IL-1 receptor antagonist, tumor necrosis factor-α, and C-reactive protein], and were tested in multivariable logistic regression models implementing multiple imputation and validation by 100-fold bootstrap resampling. RESULTS: )] and physically inactive (53.5% did not meet World Health Organization recommendations). Clinical and behavioral associations with CRF at year 2 included pre-treatment CRF [aOR versus no pre-treatment CRF: 3.99 (95% CI 2.81-5.66)], younger age [aOR per 1-year decrement: 1.02 (95% CI 1.01-1.03)], current tobacco smoking [aOR versus never: 1.81 (95% CI 1.26-2.58)], and worse insomnia or pain [aOR per 10-unit increment: 1.08 (95% CI 1.04-1.13), and 1.12 (95% CI 1.04-1.21), respectively]. Secondary analyses indicated additional associations of IL-2 [aOR per log-unit increment: 1.32 (95% CI 1.03-1.70)] and IL-10 [0.73 (95% CI 0.57-0.93)] with global CRF and of C-reactive protein [1.42 (95% CI 1.13-1.78)] with cognitive CRF at year 2. Emotional distress was consistently associated with physical, emotional, and cognitive CRF. CONCLUSIONS: This study proposes a bio-behavioral framework linking pre-treatment systemic inflammation with CRF of clinical importance 2 years later among a large prospective sample of survivors of breast cancer.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".