Screening for Distress in Lung and Breast Cancer Outpatients: A Randomized Controlled Trial
Bibliographic record
Abstract
PURPOSE: Distress has been recognized as the sixth vital sign in cancer care and several guidelines recommend routine screening. Despite this, screening for distress is rarely conducted and infrequently evaluated. METHODS: A program of routine online screening for distress was implemented for new patients with breast and lung cancer. Patients were randomly assigned to one of three conditions: (1) minimal screening: the distress thermometer (DT) only plus usual care; (2) full screening: DT, problem checklist, Psychological Screen for Cancer part C measuring anxiety and depression, a personalized report summarizing concerns and the report on the medical file; or (3) triage: full screening plus optional personalized phone triage with referral to resources. Patients in all conditions received an information packet and were reassessed 3 months later with the full screening battery. RESULTS: Five hundred eighty-five patients with breast cancer and 549 patients with lung cancer were assessed at baseline (89% of all patients), and 75.5% retained for follow-up. High prevalence of baseline distress was found across patients. Twenty percent fewer patients with lung cancer in triage continued to have high distress at follow-up compared to those in the other two groups, and patients with breast cancer in the full screening and triage conditions showed lower distress at follow-up than those in minimal screening. The best predictor of decreased anxiety and depression in full screening and triage conditions was receiving a referral to psychosocial services. CONCLUSION: Routine online screening is feasible in a large cancer center and may help to reduce future distress levels, particularly when coupled with uptake of appropriate resources.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".