Role of alexithymia in predicting psychological symptoms in patients with breast and prostate cancer
Bibliographic record
Abstract
Background: Identifying the psychological factors involved in psychological problems of patients with cancer is very important. Objective: The aim of this study was to determine the role of alexithymia in predicting psychological symptoms in patients with cancer. Methods: This cross sectional study was conducted in 102 patients with cancer selected by convenience sampling method in Ardabil during 2014. The measurement tools were the Persian version of Toronto Alexithymia Scale (TAS-20) and the Hopkins Symptom Checklist-25 (SCL-25). Data were analyzed using Pearson's correlation coefficient and regression analysis. Findings: There was significantly positive correlation between alexithymia and all psychological symptoms. In regression analysis, alexithymia was predictor of all psychological symptoms. Conclusion: With regards to the results, it seems that alexithymia is able to predict psychological symptoms. Therefore, paying more attention to psychological determinants in patients with cancer and providing appropriate treatment strategies can be effective to alleviate the mental suffering.
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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 | 0.000 |
| 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) | 0.001 | 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".