Impact of early palliative care according to baseline symptom severity: Secondary analysis of a cluster‐randomized controlled trial in patients with advanced cancer
Bibliographic record
Abstract
BACKGROUND: Early palliative care (EPC) improves the quality of life but may not be feasible for all patients with advanced cancer. Symptom screening has been suggested to triage patients for EPC, but scant evidence exists for this practice. METHODS: We conducted a subgroup analysis of a cluster-randomized controlled trial of EPC vs. standard oncology care according to patients' baseline symptom scores (high [>23] vs. low [≤23] Edmonton Symptom Assessment System Distress Score [ESAS SDS]). A linear mixed-effects model was used to account for correlation within clusters, adjusting for the baseline outcome score and all covariates in the original trial. RESULTS: Among the 461 participants, baseline symptom scores were high in 229 patients (127 intervention, 102 control) and low in 232 (101 intervention and 131 control). Among those with high baseline symptoms, there was improved quality of life in the EPC arm compared to controls at 4 months (adjusted difference in primary outcome of FACIT-Sp change score [95% CI], 8.7 [2.8 to 14.5], p = 0.01; adjusted difference in QUAL-E, 4.2 [0.9-7.5], p = 0.02); there was also improved satisfaction with care (6.9 [3.8-9.9], p = 0.001) and clinician-patient interactions (-1.7 [-3.4 to -0.1], p = 0.04), but no significant difference in ESAS SDS (-5.6 [-12.7 to 1.4], p = 0.11). In the low baseline symptom group, there were no significant differences between arms for any outcomes. CONCLUSION: EPC improved quality of life, satisfaction with care, and clinician-patient interactions only in those with high baseline symptoms. Symptom severity may be an appropriate criterion to trigger early referrals to palliative care.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".