Determinants of Adjustment Problems in Hemodialysis Patients: The Role of Medical Trauma and Cognitive Impairments
Bibliographic record
Abstract
The objective of the study was to explore the Medical Trauma and Cognitive Problems as a significant determinant of Adjustment Problems in Hemodialysis patients. It was cross sectional study in which 200 hemodialysis patients were evaluated from either gender over the age older than 19 were studied. There were 52% male participants and 48% female participants. Hemodialysis patients were studied from different hospitals of Pakistan, such as Sialkot Kidney Hospital, Saad Hospital Daska, Hameeda Bashir Hospital Daska, District Head Quarter Daska, Bhati Hospital Gujranwala and District Head Quarter Gujranwala. The Scale of Adjustment for Adults, Experience of Medical Trauma Scale (EMTS) and Urdu version of the Montreal Cognitive Assessment was used in the study. Consent form and demographic information was also taken. The Multiple linear regression and Neural Network analysis was applied to examine the hypotheses. Results has confirmed that medical trauma and cognitive problems were the significant predictor of adjustment problems in hemodialysis patients [R²=.285; F (2, 197) = 39.309, p<.01]. Trauma can alter how we remember specific events from the past. Findings explained 28.5% variation in the adjustment problems of hemodialysis patients was because of cognitive problems and medical trauma. According to the percentage, medical trauma is the most significant predictor of adjustment problems, with a 0.641 (normalized importance of 100%) followed by cognitive problems, 0.359 (normalized importance of 56%). Among the factors, medical trauma contributes more to adjustment problems than cognitive problems do both factors were influencing and predicting the issues related to adjustment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".