High prevalence of persistent emotional distress in desmoid tumor
Bibliographic record
Abstract
OBJECTIVE: Clinical experience suggests a high prevalence of emotional distress in patients with desmoid tumor (DT). We examine longitudinal Distress Assessment and Response Tool (DART) scores to estimate prevalence and persistence of distress, and compare cross-sectional data between DT and malignant sarcoma cohorts, to identify predictors of distress. METHODS: Patients with DT completed DART at: T1-diagnosis, T2-during, T3-<6 months, and T4-≥6 months, post-treatment. DART includes patient-reported outcome measures of physical symptoms (ESAS-r), depression (PHQ-9), anxiety (GAD-7), and social difficulties (SDI-21). Descriptive prevalence and persistence of anxiety, depression, and wellbeing are reported, and mixed model regression analyses determine predictors of distress. RESULTS: Between 2012 and 2018, a total of 152 DART screens from 94 patients with DT were completed (T1: n = 44, T2: n = 31, T3: n = 22, T4: n = 55). Patients had a mean age 40 years, 78% were female and DT locations were abdominal wall (48%), extremity (30%), and mesentery (22%). Moderate to severe ESAS-r scores (≥4) persisted at T4 for anxiety (20%), depression (13%), and poor wellbeing (31%). Compared to 402 patients with malignant sarcoma, patients with abdominal wall sited DT reported severe PHQ-9 and GAD-7 scores twice as frequently. Abdominal wall location, female sex, history of mood problems, and psychosocial concerns were significant predictors of anxiety, depression, and poor wellbeing in DT. CONCLUSIONS: Adults with DT experience persistently high emotional distress compared to patients with malignant sarcoma. Women with abdominal wall DT, prior mood, and current psychosocial concerns need early attention within multidisciplinary treatment settings to reduce persistent distress.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".