Influence of Selected Biomarkers on Stress and Alexithymia in Patients Under Hemodialysis Treatment
Bibliographic record
Abstract
BACKGROUND: Hemodialysis treatment and the high stress brought about by the treatment are the circumstances which set the background for alexithymia. Alexithymic feelings basically emerge as restriction in the world of emotion and thought, and the inability to recognize physiological changes. Biomarkers that are indicators of physical change are influential in the stress lives of individuals. This research was carried out to discover the relationships between, and to determine the influence of urea, creatinine, sodium, potassium, hemoglobin, hematocrit, albumin, calcium, phosphorus and C-reactive protein biomarkers on stress and alexithymia in individuals, who are diagnosed with chronic renal failure and receive hemodialysis treatment. METHODS: The research environment was formed of patients who underwent hemodialysis treatment in a hospital in Turkey. The subject group was comprised of 72 individuals. Demographic data form, biomarker list form, Hemodialysis Stressor Scale and Toronto Alexithymia Scale were used in the research. RESULTS: < 0.05). CONCLUSION: It was concluded that stress and alexithymic feelings were high in patients who received hemodialysis treatment, and that there is a relationship between C-reactive protein, creatinine, sodium, hemoglobin, hematocrit, potassium from the biomarkers and the scales and scale sub-dimensions. It is necessary to increase the awareness of nurses on the importance of the skills to communicate with individuals who have to cope with stress, manage emotions, and have high stress and emotional deprivation.
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 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".