Assessing the psychosocial needs of newly diagnosed patients with nonsmall cell lung cancer: Identifying factors associated with distress
Bibliographic record
Abstract
OBJECTIVE: The Psychosocial Screen for Cancer (PSSCAN-R) questionnaire is a validated screening tool used to identify the psychosocial needs of patients with cancer. It assesses patients' perceived social supports and psychosocial needs, and the presence of symptoms of depression and anxiety. The study goals were to assess the prevalence and factors associated with distress in patients with newly diagnosed nonsmall cell lung cancer (NSCLC). METHODS: All patients with NSCLC referred to BC Cancer centers from 2011 to 2015, who completed a prospective PSSCAN-R questionnaire at the time of their first visit, were included in the study. Demographics and baseline disease characteristics were collected retrospectively. The chi-squared test, Fisher exact test, and logistical regression analysis were used to compare factors associated with the presence of distress based on sex, age, stage of disease, and performance status (PS). RESULTS: A total of 4281 NSCLC patients completed the PSSCAN-R questionnaire. Baseline characteristics: 70% were greater than or equal to 65, 50% female, 52% metastatic disease, 47% Eastern Cooperative Oncology Group (ECOG) greater than or equal to two. Patients who were female, less than 65, have metastatic disease and poor PS were more likely to report subclinical or clinical symptoms of anxiety. Symptoms of depression were associated with younger, female, poor PS patients, and social isolation. CONCLUSIONS: Newly diagnosed patients with NSCLC are likely to report clinical symptoms of anxiety and depression and have a high number of concerns in multiple psychosocial domains. Resource development for lung cancer patients should be based on their care needs with careful consideration of patients' age, gender, stage, and social situation to optimally support their psychosocial needs during treatment and follow-up.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".