Chemotherapy-induced peripheral neuropathy and its association with quality of life among cancer patients
Bibliographic record
Abstract
Background and objective: Chemotherapy-induced peripheral neuropathy (CIPN) is a common incapacitating complication of various chemotherapeutic agents that severely impact the patient’s quality of life. Most of patients treated with anticancer agents develop CIPN early after treatment and may necessitate dose modification or termination, which can increase cancer-related morbidity and mortality. Aim: investigate the Chemotherapy-Induced Peripheral Neuropathy and its Association with Quality of Life among Cancer patients.Methods: A descriptive study design was applied in this study, on a purposeful sample of 250 adult patients diagnosed with chemotherapy induce peripheral nephropathy. The study instruments were the demographic and medical history questionnaire, PNQ, EORTC CIPN20 and EORTC30.Results: Symptoms severities mean score is 5.58 ± 2.97. Sensory neuropathy registered the highest mean at 21.23 points, followed by motor (17.33) and autonomic (5.11). About one quarter of participants reported poor global quality of life. Poor physical function was reported by 22.3% of all participants. Fatigue, pain and insomnia were the most common symptoms suffered by patients. There is a relation between CIPN and duration of cancer diagnosis, type of cancer, intervention, gender, and other condition.Conclusions: CIPN is the furthermost common complication of chemotherapy that affects patient’s QoL. Assessment of chemotherapy-related peripheral neuropathy helps clinicians to develop and evaluate much needed targeted therapies and to help improving QoL.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".