Online screening for distress, the 6th vital sign, in newly diagnosed oncology outpatients: randomised controlled trial of computerised vs personalised triage
Bibliographic record
Abstract
BACKGROUND: This randomised controlled trial examined the impact of screening for distress followed by two different triage methods on clinically relevant outcomes over a 12-month period. METHODS: Newly diagnosed patients attending a large tertiary cancer centre were randomised to one of the two conditions: (1) screening with computerised triage or (2) screening with personalised triage, both following standardised clinical triage algorithms. Patients completed the Distress Thermometer, Pain and Fatigue Thermometers, the Psychological Screen for Cancer (PSSCAN) Part C and questions on resource utilisation at baseline, 3, 6 and 12 months. RESULTS: In all, 3133 patients provided baseline data (67% of new patients); with 1709 (54.5%) retained at 12 months (15.4% deceased). Mixed effects models revealed that both groups experienced significant decreases in distress, anxiety, depression, pain and fatigue over time. People receiving personalised triage and people reporting higher symptom burden were more likely to access services, which was subsequently related to greater decreases in distress, anxiety and depression. Women may benefit more from personalised triage, whereas men may benefit more from a computerised triage model. CONCLUSION: Screening for distress is a viable intervention that has the potential to decrease symptom burden up to 12 months post diagnosis. The best model of screening may be to incorporate personalised triage for patients indicating high levels of depression and anxiety while providing computerised triage for others.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 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".