Impact of Oncological Treatment on Quality of Life in Patients with Head and Neck Malignancies: A Systematic Literature Review (2020–2025)
Bibliographic record
Abstract
Background: Quality of life (QoL) is a critical indicator in assessing the success of oncological treatments for head and neck malignancies, reflecting their impact on physiological functions and psychosocial well-being beyond mere survival. Treatments (surgery, radiotherapy, chemotherapy) pose multiple functional and emotional challenges, and recent advancements underscore the necessity of evaluating post-treatment QoL. Objective: This literature review investigates the impact of oncological treatment on the QoL of patients with malignant head and neck cancers (oral, oropharyngeal, hypopharyngeal, laryngeal) and identifies factors influencing their QoL index. Methodology: Using a PICO framework, studies from PubMed Central were analyzed, selected based on inclusion (English publications, full text, PROM results) and exclusion criteria. The last research was conducted on 6 April 2025. From 231 identified studies, 49 were included after applying filters (MeSH: “Quality of Life,” “laryngeal cancer,” “oral cavity cancer,” etc.). Data were organized in Excel, and the methodology adhered to PRISMA standards. Results: Treatment Impact: Oncological treatments significantly affect QoL, with acute post-treatment declines in functions such as speech, swallowing, and emotional well-being (anxiety, depression). Partial recovery depends on rehabilitative interventions. Influencing Factors: Treatment type, disease stage, socioeconomic, and demographic contexts influence QoL. De-escalated treatments and prompt rehabilitation improve recovery, while complications like trismus, dysphagia, or persistent hearing issues reduce long-term QoL. Assessment Tools: Standardized PROM questionnaires (EORTC QLQ-C30, QLQ-H&N35, MDADI, HADS) highlighted QoL variations. Studies from Europe, North America, and Asia indicate regional differences in outcomes. Limitations: Retrospective designs, small sample sizes, and PROM variability limit generalizability. Multicentric studies with extended follow-up are recommended. Conclusions: Oncological treatments for head and neck malignancies have a complex impact on QoL, necessitating personalized and multidisciplinary strategies. De-escalated therapies, early rehabilitation, and continuous monitoring are essential for optimizing functional and psychosocial outcomes. Methodological gaps highlight the need for standardized research.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".