Nursing interventions in autologous stem cell transplantation for autoimmune diseases
Bibliographic record
Abstract
AIMS: To identify clinical symptoms and nursing interventions for stem cell therapy in autoimmune diseases. DESIGN: This is a retrospective, cross-sectional study. METHODS: This study was undertaken with patients diagnosed with type 1 diabetes or multiple sclerosis, undergoing autologous haematopoietic stem cell transplantation from January 2004 - December 2018. Data were registered in a questionnaire, taken during the conditioning regimen comprising cyclophosphamide and rabbit anti-thymocyte globulin. Descriptive statistics and Fisher's exact test were used for data analysis. RESULTS: There were 68 and 23 patients in the multiple sclerosis and type 1 diabetes groups respectively. Skin rash, nausea, vomiting and fever were more frequent and diverse in the type 1 diabetes group. Steroids were used as prophylaxis for anti-thymocyte globulin-associated allergic reactions in 97% of multiple sclerosis patients. Most of the identified symptoms and nursing interventions were more associated with one or other disease group (p < .05) and were more frequent in the type 1 diabetes group. CONCLUSION: Patients with autoimmune diseases who underwent stem cell therapy present differences in their repertoire of adverse events and require disease-specific nursing actions. IMPACT: Our results may enable nurses to establish transplant and disease-specific guidelines to improve prevention and management of adverse events and therefore optimize patient care and therapeutic success.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".