The Relation between the Severity of Pain and Common Symptoms in Patients with Metastatic Cancer
Bibliographic record
Abstract
Background: There is a relation between the severity of pain and common symptoms in patients with metastatic cancer.Aim: This study was done to explore this relation among Egyptian patients with advanced cancer.Methods: The study included 120 adult metastatic cancer patients with pain from two cancer centers in Cairo and Sharkia,Egypt. Pain and other common symptoms were assessed using the Arabic version of the Edmonton Symptom AssessmentSystem (ESAS). The Eastern Cooperative Oncology Group (ECOG) performance scale was used to assess performancestatus.Results: The prevalence of ESAS symptoms was high among patients with cancer pain (tiredness, 94%; drowsiness, 63%;nausea, 60%; lack of appetite, 77%; shortness of breath, 53%; depression, 88%; anxiety, 83%; poor wellbeing, 96%). TheECOG performance scale was 1 in 21 (17.5%) patients, 2 in 57 (47.5%), 3 in 38 (31.7%) and 4 in 4 (3.3%). The averageESAS score was 33.9 ± 13.8, 48.9 ± 14.7, 58 ± 15.4 and 70 ± 5.5 among patients with ECOG score 1, 2, 3 and 4;respectively (p < 0.001). There was no significant difference in the average score of any of the ESAS items according tothe site of metastases. There was a significant positive correlation between the pain score and the scores of tiredness(p < 0.001), nausea (p=0.037), lack of appetite (p < 0.001), shortness of breath (p=0.001), depression (p < 0.001), anxiety(p < 0.001) and poor wellbeing (p < 0.001).Conclusion: Egyptian patients with cancer pain experience high symptom burden. The severity of pain strongly correlateswith the presence and severity of other ESAS symptoms. Systematic assessment of other symptoms is indispensable inpatients with cancer pain for proper control of symptoms and improving quality of life.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".