Screening for Distress in Lung and Breast Cancer Outpatients: A Randomized Controlled Trial
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| 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 | 0.001 |
| 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 it