Bibliographic record
Abstract
Objective:The aim of the present study was to examine the relationships between alexithymia, negative affect,and quality of life(QOL)of lung cancer patients receiving chemotherapy.Methods:51lung cancer patients receiving chemotherapy completed questionnaires of quality of life questionnaire for Chinese cancer patients receiving chemobiotherapy(QLQ-CCC),26-item Toronto alexithymia scale(TAS-26)and symptom checklist (SCL-90).Results:1)The patients had better score in QLQ(increased by 11%,P0.05)and in each aspect of it after chemotherapy than during chemotherapy.2) Alexithymic patients had significantly poorer score in QLQ(was lower by 11%,P0.05)and in physical and psychological aspect of it than non-alexithymic patients after chemotherapy. There was no difference between the two groups during chemotherapy.3) Pearson's correlation showed that physical aspect of QLQ was significantly correlated with TAS-DIF,phase of lung cancer,SCL-somatization and SCL-depression after chemotherapy(r=-0.37～0.61,P0.05),and psychological aspect of QOL was significantly correlated with TAS-DDF,phase of lung cancer,SCL-somatization,SCL-depression and SCL-anxiety after chemotherapy(r=-0.43～0.51,P0.05).4)Stepwise linear regression showed that TAS-DIF and phase of lung cancer were independent predictive variables of physical aspect of QLQ after chemotherapy (t=3.13 and-3.81,P0.05), and phase of lung cancer and SCL-depression were independent predictive variables of psychological aspect of QLQ after chemotherapy(t=-2.42 and-2.84,P0.05).Conclusion:These results suggest that alexithymia,combined with negative affect and other factors,may have a negative effect on the recovery of physical and psychological aspect of quality of life of lung cancer patients receiving 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".