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Record W4396524563 · doi:10.4236/jct.2024.154018

Effects of Alexithymia to Stigma of Patients with Lymphedema

2024· article· en· W4396524563 on OpenAlexaboutno aff
Qiaoling Zhong, Huizhen Zhang, Liping Zhang, Qinghua Luo, Huiting Zhang, Fei‐Fei Liu, Hailin Tang, Na Li, Lijuan Zhang

Bibliographic record

VenueJournal of Cancer Therapy · 2024
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaStigma (botany)LymphedemaMedicineClinical psychologyPsychologyPsychiatryInternal medicineCancer

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.316
Threshold uncertainty score0.222

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.289
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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