Psychosocial Distress of Head Neck Cancer (HNC) Patients Receiving Radiotherapy: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Head and Neck Cancer (HNC) patients are at increased risk of psychosocial distress compared with patients with other forms of cancer. Various symptoms of the disease and side effects of treatment are attributing factors for distress. This systematic review aimed to identify the prevalence of psychosocial distress among HNC patients receiving radiotherapy. METHODS: The following search engines from 2000-2021 were searched: PubMed, CINAHL, Cochrane, Web of Science, ProQuest, Scopus, and Embase. Citation checking and extensive reference checking were also conducted. Cross-sectional, longitudinal, cohort, exploratory and prospective, repeated measure studies published in English were included. Newcastle Ottawa Scale assessed the quality, and the data were extracted on a validated data extraction form. RESULTS: Out of 782 articles, eleven records met the eligibility criteria, including 776 HNC patients receiving radiotherapy. Data were synthesized and summarized descriptively as measurements were not homogenous. Prevalence estimates of depression or depressive symptoms were calculated. Outcomes were measured with various measuring tools and reported in frequency, percentage, mean, and standard deviation in various studies. All studies reported depression ranging from 9.8% to 83.8%, and pooled estimated prevalence of depression among HNC patients receiving radiotherapy is 63% (95% CI 42-83) with significant heterogeneity (I2= 97.66%; p<0.001). An increase in the trend is observed along with treatment progression. Another three studies reported anxiety along with depression. Physical symptoms, body image, low social support, fatigue specific radiotherapy regimens were the predictive factors of depression. CONCLUSION: HNC patients are psychosocially distressed during radiotherapy, and the distress is steadily increased during the therapy. The predictive factors could serve as potential areas of intervention and supportive therapy during radiotherapy.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".