Quality of Life Among Cancer Patients Treated With Chemotherapy or Radiotherapy in Erbil City An Evaluation Study
Bibliographic record
Abstract
Objectives: This study was conducted to assess the quality of life in its different domains among cancer patients in relation to their treatment modality. Methods: A convenience sample of 200 patients with cancer was selected, 100 patients were on chemotherapy and the other 100 were on radiotherapy. Data was obtained through direct interview, using FACT-G questionnaire, version 4, and was managed through a statistical program, using appropriate statistical tests. Results: The emotional domain was the least affected one in both study groups (P = 0.800), while the physical domain reflected a significant statistical differences (P < 0.001); it was mildly affected in 92% of those with radiotherapy and moderately affected in 47% of those on chemotherapy. The social and functional domains were the most badly affected with a significant difference in the functional domain only (P <0.001) where 93% of those on radiotherapy being badly affected. The functional well-being of breast cancer was more affected among those with radiotherapy (P = 0.039), while the physical domains of quality of life of patients with gastrointestinal tract cancer was more badly affected by chemotherapy (P = 0.001). Conclusion: Patients on chemotherapy are more badly affected in some domains of quality of life, compared to those on radiotherapy. The emotional domain of QoL was the least affected, while the social and functional domains were the most badly affected ones among cancer patients, whether they were treated with radiotherapy or chemotherapy.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".