Symptom screening with Targeted Early Palliative care (STEP) versus usual care for patients with advanced cancer: A mixed methods study
Bibliographic record
Abstract
Abstract Purpose: Although early palliative care is recommended, resource limitations prevent its routine implementation. We report on the preliminary findings of a mixed methods study involving a randomized controlled trial (RCT) of Symptom screening with Targeted Early Palliative care (STEP) and qualitative interviews. Methods: Adults with advanced solid tumors and an oncologist-estimated prognosis of 6-36 months were randomized to STEP or symptom screening alone. STEP involved symptom screening at each outpatient oncology visit; moderate to severe scores triggered an email to a palliative care nurse, who offered referral to in-person outpatient palliative care. Patient-reported outcomes of quality of life (FACT-G7), depression (PHQ-9), symptom control (ESAS-r-CS), and satisfaction with care (FAMCARE P-16) were measured at baseline and 2, 4, and 6 months. Semi-structured interviews were conducted with a subset of participants. Results: From Aug/2019 to Mar/2020 (trial halted due to COVID-19 pandemic), 69 participants were randomized to STEP (n=33) or usual care (n=36). At 6 months, 45% of STEP arm patients and 17% of screening alone participants had received palliative care (p=0.009). Nonsignificant trends for all outcomes favored STEP: difference in change scores for FACT-G7=1.67 (95% CI: -1.43,4.77); ESAS-r-CS=-5.51(-14.29,3.27); FAMCARE P-16=4.10(-0.31,8.51); PHQ-9=-2.41 (-5.02,0.20). Sixteen patients completed qualitative interviews, describing symptom screening as helpful to initiate communication; triggered referral as initially jarring but ultimately beneficial; and referral to palliative care as timely. Conclusion: STEP improves access to palliative care. Despite lack of power, preliminary results are encouraging and qualitative results demonstrate acceptability. Findings will inform an RCT of combined in-person and virtual STEP. ClinicalTrials.gov Identifier: NCT03987906
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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 | 0.027 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".