Predictors of biopsychosocial distress in women with locally advanced and/or metastatic breast cancer
Bibliographic record
Abstract
Objective: The objective of this study was to identify predictors of biopsychosocial distress in women with locally advanced and/or metastatic breast cancer. Methodology: This is a quantitative cross-sectional study carried out with 125 women with locally advanced and/or metastatic breast cancer. The Palliative Performace Scale, the Edmonton Symptom Assessment Scale, and a sociodemographic questionnaire were used. Data were analyzed using descriptive and inferential statistics. We used the Shapiro-Wilk test and the Spearman correlation matrix. Results: The performance of patients had a mean of 39 and median of 40 (0–100), and survival after referral to palliative care was 75.96 days, median 13 (SD 144.73; 1–618). The most intense symptoms were lack of appetite (mean 6.59; SD 3.58; 0–10), anxiety (mean 6.05; SD 3.76; 0–10), and fatigue (mean 5.86; SD 3.63; 0–10). Pain and nausea were correlated with worse performance (p<0.05) and distress with worse fatigue, sadness, anxiety, lack of appetite, dyspnea, and malaise (p<0.05). Conclusion: Our results suggest that younger patients are more prone to psychosocial distress, especially showing greater lack of appetite, anxiety, and fatigue. For equitable and comprehensive care, it is necessary to implement symptom screening strategies, as well as interprofessional management, according to the correlation between experienced 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".