Factors Associated With Body Image Distress in Patients With Head and Neck Cancer: Protocol for a Systematic Review
Bibliographic record
Abstract
Background: Body image distress (BID) is a significant psychological issue for patients with head and neck cancer (HNC) and arises from the visible disfigurements and functional impairments often associated with the disease and its treatment. Understanding the factors contributing to BID in this population is crucial for developing effective interventions and support mechanisms. Objective: This systematic review outlines methods for identifying, evaluating, and synthesizing the available evidence on factors associated with BID among patients with HNC. This review intends to explore both clinical and psychosocial variables that may influence body image perceptions and the resulting psychological impact. Methods: This review will follow the Joanna Briggs Institute (JBI) Reviewer's Manual for Systematic Reviews Concerning Etiology and Risk Factors. PsycINFO, MEDLINE, EBSCOhost CINAHL, Web of Science, Cochrane Library, and Embase were searched for relevant studies from inception to December 2024. All in-depth quantitative analyses, descriptive observational studies, experimental studies, and quasi-experiments published in English or Chinese were analyzed and described. Studies examining factors associated with BID in patients with HNC were included. The population, exposure, comparison, and outcome (PECO) format was used to develop the search strategy. For different databases, search terms will be combined using Boolean operators. The JBI Risk of Bias Tool was used to evaluate bias risk in the included studies. The extracted data will include basic study information, research design, sample characteristics, BID measurement tools, primary outcomes, statistical analysis methods, and quality assessment results. Subgroup and meta-regression analyses will be performed on different therapies, treatment stages, genders, ages, cultural backgrounds, etc. I2 statistics will be used to evaluate heterogeneity, and funnel plots will address publication bias. If we detect significant heterogeneity, the findings will be reported as a systematic review without a meta-analysis. Results: The database search will be conducted in October 2025. It is anticipated that the study findings will be submitted for publication in a peer-reviewed journal by the end of March 2026. Conclusions: This study will summarize the factors that can help identify and evaluate the factors associated with BID in patients with HNC, providing up-to-date evidence to inform the management of body image in this patient population.
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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 | 0.074 | 0.086 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.020 | 0.020 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.057 | 0.007 |
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".