Predicting the Quality of Life in Patients with Gastrointestinal Cancers in Iranian Population
Bibliographic record
Abstract
BACKGROUND: Evaluation of the quality of life (QoL) of cancer patients gives valuable information regarding the burden of disease, type of treatment and its side effects in studies on chronic diseases. In this study an attempt is made to predict the QoL of patients with gastrointestinal (GI) cancer.METHODS: In a cross sectional prospective study, data of 107 patients with GI cancers 58 male & 49 female with a mean age of 60.7±10.5 referred to a hospital from April 2009 to June 2015 were collected by using two standard questionnaires of EORTC-QLQ C30 and EORTC-QLQ-GINET 21. Data analysis was done using multivariate analysis of variance (MANOVA) and correlation test in soft wares of STATA and SPSS.16.RESULTS: Total QoL score was 50.7±24.6 in patients with gastrointestinal cancer which is almost optimal. Symptoms level was more in gastric cancer patients and their major complaints were constipation and diarrhea. QoL of patients with GI cancers were more affected by the educational level and type of cancer (P<0.05).A high correlation of QoL was related to social and emotional domains (P<0.05). Mean score of QoL was more in males with colorectal cancer, educated and retired patients (P<0.05).CONCLUSION: the social and emotional support to patients with esophageal cancer and to reduce the level of symptoms in patients with gastric cancer due to complications of their diseases or treatment should be of consideration to improve their QoL.
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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 | 0.003 |
| 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".