Care Needs and Symptoms Burden of Breast Cancer Patients in Jordan: A Cross-Sectional Study
Bibliographic record
Abstract
Breast cancer is the most common malignancy in Jordan. Rigorous needs assessment for breast cancer patients can prioritize both cancer care and palliative care to propose the appropriate services effectively. We conducted a cross-sectional study of breast cancer patients in a cancer center in Jordan. We assessed symptom burden, comorbidities, and performance using the Edmonton Symptom Assessment System (ESAS), the Charlson Comorbidity Index (CCI), and the Australia-modified Karnofsky performance scale (AKPS). Descriptive analysis and regression models to predict the highest symptom burden were used. A total of 233 participants were enrolled: curative vs. palliative intent groups (147 (63%) vs. 86 (37%) patients), respectively. Tiredness was the most reported symptom in 189 patients (81%), while nausea was the least in 61 patients (26.2%). A relationship between the AKPS score and total ESAS was seen (correlation coefficient of −0.487; p < 0.0001). The prevalence of anxiety (p = 0.014), lack of appetite (p = 0.002), poor well-being (p < 0.001), and sleep disorder (p = 0.035) was higher in the palliative care intent group than in the curative one. We identified unmet needs in breast cancer patients. Both groups showed a prevalence of distressing symptoms suggesting that even those with non-palliative intent have high needs and should receive integrated palliative care.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".