Psychosocial Distress Scores and Needs among Newly Diagnosed Sarcoma Patients: A Provincial Experience
Bibliographic record
Abstract
INTRODUCTION: Information on the psychosocial distress and needs of sarcoma patients at diagnosis is sparse. The Canadian Problem Checklist (CPC) and Psychosocial Screen for Cancer-Revised (PSSCAN-R) are validated tools to identify cancer patients' distress and are administered to all new patients referred to BC Cancer prior to their consultation. We used the CPC and PSSCAN-R to understand sarcoma patients' needs at the initial oncology consultation in British Columbia, Canada. MATERIALS AND METHODS: All sarcoma patients who completed the CPC and PSSCAN-R within 6 months of diagnosis between 2011 and 2016 were included. The retrospective chart review identified baseline demographics: age, performance status, disease location, resectability, and histology. Analysis was conducted using descriptive statistics, chi-squared test, Fisher's exact test, and Kaplan-Meier method. RESULTS: 413 sarcoma patients were identified. The majority of patients were over the age of 40 (83.3%) with ECOG performance status 0-1 (82.6%) and lower extremity tumors (55.4%). The most common diagnoses were liposarcoma 21.3%, undifferentiated pleomorphic sarcoma 12.1%, and myxofibrosarcoma 11.1%. At the initial consultation, 42.6% of patients were deemed resectable, 8.5% unresectable/metastatic, and 48.9% required further staging investigations. The top three patient-reported distress symptoms were feeling tense and unable to relax (50%), feeling nervous and shaky (48%), and experiencing repetitive and scary thoughts (42%). 38% of patients had subclinical/clinical anxiety symptoms, and 21% of patients had subclinical/clinical depression symptoms. 5% of patients expressed suicidal ideation. The top three concerns/needs were understanding of illness/treatment (45.5%), fear/worries (45.3%), and worry about family (23%). No differences in overall survival were identified for patients displaying symptoms of depression or anxiety versus no symptoms. DISCUSSION: Up to 45% of sarcoma patients experience some form of psychological distress at disease presentation. Patients desire information about their diagnosis and treatment. Tailored interventions to individual psychological comorbidity and improved patient education resources would be beneficial.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".