Screening for Body Image Dissatisfaction in Patients with Advanced Cancer: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: Cancer and its treatment can significantly affect appearance and body integrity. A number of studies have explored the impact of cancer and its treatment on body image, primarily in head and neck and breast cancer. The aim of this pilot study was to examine the construct of body image dissatisfaction and its measurement using a single question in patients with advanced cancer. METHODS: Outpatients with advanced cancer were recruited (n=81). Assessments included Body Image Scale (BIS), Appearance Schema Inventory (ASI-R), Edmonton Symptom Assessment System (ESAS) with a total symptom distress score (TSDS) and two subscales scores (physical distress [PHS] and psychological distress [PSS]), Hospital Anxiety Depression Scale (HADS), and one question assessing the overall appearance satisfaction from the Multidimensional Body-Self Relations Questionnaire (MBSRQ). We also asked patients to rate the body image changes importance compared with five symptoms (pain, fatigue, depression, insomnia, lack of appetite). RESULTS: Forty-seven (58%) patients had a BIS score >10 (body image dissatisfaction) with a median of 11 (first-third quartiles, Q1-Q3; 5-16) and a median ASI-R of 3.1 (Q1-Q3; 2.8-3.5). Sensitivity and specificity of ≤3 for body image dissatisfaction in the single overall appearance question using the BIS as a standard was 0.70 and 0.71, respectively. BIS score was significantly correlated with ASI-R (r=0.248; p=0.025), age (r=-0.225; p=0.043), HADS-A (r=0.522, p<0.001), HADS-D (r=0.422, p<0.001), PSS score (r=0.371, p=0.001), PHS score (r=0.356, p=0.001), TSDS score (r=0.416, p<0.001), and the overall appearance question (MBSRQ; r=-0.449, p<0.001). CONCLUSION: Body image dissatisfaction was frequent and associated with symptom burden. A single item ≤3 has a sensitivity of 70% for body image satisfaction screening.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".