Efficiency of Hypnotherapy on Reducing Pain and Death Anxiety, and Increasing Resilience and Improvement of Cancer Cells in Patients with Acute Myeloid Leukemia
Bibliographic record
Abstract
Background and purpose: Leukemia is a group of cancers caused by accumulation of malignant white blood cells in the blood or bone marrow. The aim of this study was to investigate the efficiency of hypnotherapy on pain relief, death anxiety, resilience, and healing of cancer cells in patients with acute myeloid leukemia treated with chemotherapy. Materials and methods: A quasi-experimental study was carried out in which the research population were 86 patients of whom 26 (aged 30-50 years old) were selected via convenience sampling. They were randomly assigned into either experimental group or control group. Flow cytometry tests were done to confirm acute myeloid leukemia. The McGill Pain Management, Connor-Davidson Resilience scale, and the Collett-Lester Fear of Death Scale were administered to collect the data. Hypnosis therapy (six sessions) was done in experimental group. Data were analyzed applying analysis of covariance in SPSS V22. Results: Hypnosis therapy was found to have significant effects on mental dimensions in experimental group compared to the control group (P= 0.039). Follow-up investigations showed more changes in death anxiety compared with other two dimensions in experimental group (6.67). Laboratory results indicated the onset of inflammatory reaction in experimental group. Conclusion: Hypnotherapy is a powerful method in caring for cancer treatment.
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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 | 0.000 | 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".