Effects of Alexithymia to Stigma of Patients with Lymphedema
Bibliographic record
Abstract
BACKGROUND: Secondary lymphedema is one of the common complications after malignant tumor surgery. It is a chronic and complex disease. Once lymphedema occurs, there will be discomfort such as limb swelling, pain, numbness and tension, which will eventually lead to changes in the appearance of the affected limb and will seriously affect the quality of life and require lifelong treatment and psychosocial support. This study investigated the current situation of stigma and alexithymia in patients with lymphedema, and discussed the impact of alexithymia on stigma in patients with lymphedema. AIMS: To understand the current situation of stigma and alexithymia in patients with lymphedema, and to analyze the influence of alexithymia on stigma. METHODS: 195 patients with lymphedema in a hospital were selected by convenient sampling. General information questionnaire, Toronto Alexithymia Scale and social impact scale were used to investigate respectively, to study the general situation, stigma and alexithymia of the respondents. RESULTS: The results showed that the total score of stigma in 195 patients with lymphedema was (60.36 ± 11.08), and the total score of alexithymia was (56.53 ± 8.43). Multiple linear regression analysis showed that alexithymia and family relationship were the influencing factors of stigma in patients with lymphedema. CONCLUSIONS: The patients with lymphedema have obvious stigma, and alexithymia and family relationship are the influencing factors.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".