Pharmacological Interventions For Pain And Fatigue Management In Elderly Leukemia Patients: A Comparative Study Of Analgesic And Antidepressant Efficacy
Bibliographic record
Abstract
Older leukemia patients frequently endure cancer-related pain and fatigue, impairing their daily lives and reducing treatment adherence. Pharmacological intervention is a key approach to alleviating these symptoms. This study focuses on the efficacy and safety of analgesic and anti-fatigue drugs in elderly leukemia patients, aiming to optimize drug selection for precision treatment. From January 2022 to June 2024, Taixing People's Hospital enrolled 82 elderly leukemia patients with pain and fatigue, dividing them into a control group (no analgesics) and a celecoxib group. Clinical outcomes: Pain relief, fatigue reduction, depression improvement, quality of life and adverse reactions were compared at 1, 4, and 8 weeks. The celecoxib group showed significant improvements in pain, fatigue, anxiety, and depression, with better scores on the Numeric Rating Scale (NRS), Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F), Edmonton Symptom Assessment System-revised (ESAS-r), the Hospital Anxiety and Depression Scale-Anxiety (HADS-A) and the Hospital Anxiety and Depression Scale-Depression (HADS-D) scales at all-time points (P<0.05). Their Quality of Life Questionnaire Core 15 Palliative (QLQ-C15-PAL) scores also indicated enhanced functional and overall quality of life, with lower symptom scores compared to the control group (P<0.05). No serious adverse events occurred, confirming celecoxib's safety.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".