Biopsychosocial Markers of Body Image Concerns in Patients with Head and Neck Cancer: A Prospective Longitudinal Study
Bibliographic record
Abstract
(1) Background: Patients and survivors of head and neck cancer (HNC) are at a high risk of developing body image concerns. Despite the prevalence of body image concerns in patients with HNC, there is a lack of longitudinal research exploring the wide array of its associated determinants. The current longitudinal study examined the determinants and longitudinal course of body image dissatisfaction in patients with HNC. (2) Methods: Patients participated in Structured Clinical Interviews and self-administered questionnaires at four time-points: (T1) upon cancer diagnosis, (T2) at 3 months post-diagnosis, (T3) at 6 months post-diagnosis, and (T4) at 12 months post-diagnosis. They also underwent a disfigurement rating on an objective scale. (3) Results: Two hundred and twenty-four patients participated in our study. Fourteen percent to twenty-eight percent of patients reported at least moderate body image concerns across time points, with the lowest rates at baseline and the highest at 3 months (T1). It was found that patients more predisposed to developing higher levels of body image concerns presented physical markers (i.e., advanced cancer stage, lower physical functioning, higher disfigurement), psychosocial markers (i.e., higher depression, higher anxiety, and higher levels of coping with denial), and health disparities (i.e., younger age, female sex, French language, and marital status, with divorced and widowers most affected). (4) Conclusions: The findings of this study highlight the multifaceted nature of body image concerns in patients with HNC and its biopsychosocial determinants. Clinicians should pay specific attention to these biopsychosocial markers in their clinics to predict high levels of body image concerns and tailor communication/refer for support accordingly.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".