Randomized controlled trial (RCT) of symptom screening with targeted early palliative care (STEP) versus usual care in patients with advanced cancer.
Bibliographic record
Abstract
e24084 Background: To direct limited specialized palliative care resources to patients in greatest need, we developed STEP (Symptom screening with Targeted Early Palliative care). STEP entails symptom screening (ESAS-r) at each oncology clinic visit and triggered alerts (for moderate-high physical and psychological symptoms) to a nurse who calls the patient to offer a palliative care clinic (PCC) visit. We conducted a phase III RCT to assess the impact of STEP versus usual care on quality of life and other patient-reported outcomes (PROs). Methods: Adults with advanced cancer were recruited from medical oncology clinics at the Princess Margaret Cancer Centre, Toronto, Canada. Consenting patients with oncologist-assessed ECOG 0-2 and estimated survival of 6-36 months were enrolled and block randomized (stratified by tumour site and symptom severity) to STEP or usual care. Participants completed measures of quality of life (FACT-G7), depression (PHQ-9), symptom control (ESASr-CS), and satisfaction with care (FAMCARE-P16) at baseline, 2, 4 and 6 months. The primary outcome was FACT-G7 at 6 months, with a planned sample size of 261/arm. Results: From 8/2019 to 3/2020, 69 patients were enrolled: 33 randomized to STEP and 36 to usual care. The trial was then halted permanently due to the COVID-19 pandemic, owing to substantial changes to elements of STEP (shift to virtual symptom screening and palliative care) and usual care (shift to virtual oncology care). Median age was 64 years (range 25-87) and 62% (43/69) were women; study arms were balanced at baseline except gender, with more women randomized to STEP. Within the STEP arm, 20 (61%) participants triggered a nurse’s call to offer a PCC visit, of whom 13 attended the clinic at least once. All outcomes tended to be better in the STEP arm compared to usual care, particularly depression and satisfaction with care at 6 months; however, results were not statistically significant (Table). Conclusions: STEP holds promise for improving quality of life and other PROs in patients with advanced cancer and effectively directing early palliative care towards those who need it most. In response to the pandemic, an online version of STEP has been developed and a further trial is in progress. Clinical trial information: NCT03987906. [Table: see text]
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".