A nurse-delivered intervention was effective for depression in patients with cancerCommentary
Bibliographic record
Abstract
M Sharpe Professor M Sharpe, University of Edinburgh, Edinburgh, UK; Michael.Sharpe@ed.ac.uk What is the effectiveness of a complex, nurse-delivered intervention for treating major depression in patients with cancer? ### Design: randomised controlled trial (RCT) (Symptom Management Research Trials [SMaRT]). ### Allocation: {concealed}.* ### Blinding: blinded {data collectors, outcome assessors, data analysts, and safety committee}.* ### Follow-up period: 12 months. ### Setting: a regional cancer centre in the UK. ### Patients: 200 outpatients (mean age 57 y, 71% women) who had cancer with prognosis ⩾6 months and major depressive disorder (Symptom Checklist [SCL]-20 score ⩾1.75) for ⩾1 month. Exclusion criteria included epilepsy, concurrent intensive anticancer treatment (eg, frequent chemotherapy or radiotherapy), and receipt of specialist psychiatric care. ### Intervention: a nurse-delivered intervention plus usual care (n = 101) or usual care alone (n = 99). The intervention consisted …
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".