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Pharmacological Interventions For Pain And Fatigue Management In Elderly Leukemia Patients: A Comparative Study Of Analgesic And Antidepressant Efficacy

2025· article· en· W4415962371 on OpenAlexaboutno aff
Qin Li, Xin Sun

Bibliographic record

VenuePakistan Journal of Pharmaceutical Sciences · 2025
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
Fundersnot available
KeywordsAnalgesicDepression (economics)Quality of life (healthcare)Adverse effectAnxietyAntidepressantCelecoxibPsychological intervention

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.239
Threshold uncertainty score0.272

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.125
GPT teacher head0.480
Teacher spread0.355 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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