Determinants of disease acceptance in renal transplantation patients assessed with the application of Acceptance Illness Scale (AIS)
Bibliographic record
Abstract
PURPOSE: Kidney transplant patients require long-term pharmacotherapy with a significant risk of drug-related complications. The disease acceptance may significantly affect the effectiveness, safety, and patient adherence to their treatment. The purpose of this study was to evaluate, for kidney transplantation patients, the essential determinants for better disease acceptance, and whether a clinical pharmacist may influence its degree. METHODS: The study involved 201 renal graft patients aged 18-81 years. The diagnostic survey method with the questionnaire of the Acceptance Illness Scale (AIS) and authors' query was used to obtain sociodemographic and co-morbidities data, the number of medications taken, the therapy cost, a patient needs for more attention from medical staff, and their willingness to cooperate with a clinical pharmacist. RESULTS: The largest group (55.2%) of patients demonstrated a high level of acceptance of their health. However, in every disease acceptance score range (low, medium, high), the score was statistically lower in patients over 50 years of age (c2=7.27, p=0.026), occupationally inactive (c2 =13.8, p<0.001), over 5 medicines taken (c2=7.77, p=0.020), and declaring too much expenditure on the therapy (c2=14.3, p<0.001). The assessment established a statistically significant negative correlation between the number of chronic conditions and the AIS score (R=-0.32, p<0.001). The lower number of coexisting chronic diseases the better disease acceptance. Moreover, patients reporting the need for more attention from the health service and willing to consult a pharmacist cope in a statistically significant way worse with accepting their health (c2=15.1 and p<0.001, c2=6.76 and p=0.034 respectively). Conclusion: For post-transplantation patients, factors affecting the acceptance of illness should be taken into consideration while planning medical care. The reported need for professional assistance indicates necessity for establishing a multidisciplinary therapeutic team in which a clinical pharmacist should play a special role.
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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 | 0.004 |
| 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 | 0.001 |
| 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".