Association of Ego Defense Mechanisms with Electrolyte and Inflammation Marker Levels, Interdialytic Weight Gain, Depression, Alexithymia, and Sleep Disorders in Patients Undergoing Chronic Hemodialysis
Bibliographic record
Abstract
Background/Objectives: Ego defense mechanisms are subconscious processes that help individuals cope with stressors from both external and internal realities. They are divided into three levels based on their adaptive function. Patients undergoing chronic hemodialysis are those who have been treated with this method for longer than three months. Only a few studies have examined the defense mechanisms in hemodialysis patients. Our study aimed to examine the association between ego defense mechanisms and alexithymia, depression, and sleep disorders, as well as clinical and biochemical variables, in a group of 170 hemodialysis patients. Methods: We used the Defense Style Questionnaire-40, the Toronto Alexithymia Scale-26, the Pittsburgh Sleep Quality Index, and the Hamilton Depression Inventory as our analyses methods. Clinical and biochemical variables, along with interdialytic weight gain, were measured before the hemodialysis session. Results: There was a positive correlation between the affect displacement and dissociation with leukocyte levels (Spearman’s rho = 0.192, p = 0.02; rho = 0.165, p = 0.04), and between autistic fantasy and phosphorus levels (rho = −0.163, p = 0.04). Depressive HD patients had higher levels of somatization, affect displacement, and splitting compared to the HD patients without depression (Man–Whitney U test, p = 0.005, p = 0.022, p = 0.045). There were higher levels of immature defense mechanisms in the group of patients with alexithymia than in the group without alexithymia (Mann–Whitney U test, p < 0.001). Conclusions: The immature defense mechanisms were our research model’s strongest predictive factor of alexithymia (OR = 1.87, 95% CI 1.27 to 2.75).
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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".