Behavioral Characteristics,Alexithymia and Coping Styles of Patients with Acute Leukemia
Bibliographic record
Abstract
Objective: To investigate psychosocial factors of acute leukemia patients.Methods: 59 patients with acute leukemia(AL) ,45 hematopathy patients with non-malignant tumor (NMT) and 63 health persons were included and tested with three Questionnaires (Behavioral Characteristics Questionnaire ,Totronto Alexithymia scale,simple coping style questionnaire). Results: AL group had lower scores in Expression of Anger Inward (14.2±2.8)and total of Alexithymia(68.8±8.7) than NMT group(15.2±2.1,73.3±8.5, P0.05),and had higher scores in Optimism and Social Support than health group(21.9±3.6/20.6±3.0,17.7±2.1/16.4±2.4,P0.05).AL patients used less coping strategies than other two groups. Mutual-factors Analysis using Logistic regression model revealed three variables that demonstrated a statistically significant discrimination between AL group and NMT group:Age (OR=0.93), Expression of Anger Inward (OR=0.77)and Emotional Control(OR=1.05). It revealed three variables that demonstrated a statistically significant discrimination between AL group and health group: Optimism(OR=1.15), Social Support(OR=1.25) and Positive Copingstyles(OR=0.90).Conclusion: AL patients' Optimism and fantasy is likely to be helpful to fit their inner world to coping disease. It should deserved the clinician's attention to help AL patients to face disease better.
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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 | 0.000 |
| 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".