Randomized trial of a clinical nurse specialist–led enhanced survivorship and early palliative care intervention for patients with metastatic cancer.
Bibliographic record
Abstract
TPS12139 Background: While the benefits of early palliative care and clinician empathy for patients with metastatic cancer are well established, cancer survivorship remains inadequately integrated into the care of patients with distant metastases (Langbaum, N Engl J Med 380: 1300, 2019). Moreover, the optimal model of care delivery is poorly defined. Based on these data, we developed a novel multidisciplinary care model in which the radiation oncology Clinical Nurse Specialist develops therapeutic relationships with survivors with metastatic cancer and identifies and coordinates interventions to address their unmet physical and emotional issues. The goal of this intervention is to improve quality of life and overall survival. Methods: Eligible patients are adult patients with metastatic solid tumor malignancy with a predicted median survival of ≥1 year using the validated NEAT model. Using block randomization with varying block sizes of 4, 6 and 8, we plan to randomize 100 patients to either usual care or a supplemental Clinical Nurse Specialist led survivorship and palliative care intervention. Patients randomized to the Clinical Nurse Specialist have personalized coordination of services, patient education and referral to supportive care services resulting from additional in-person and phone-based touchpoints. These supplemental interactions address individual needs, such as medication side effects, physical therapy, end-of-life planning and access to community and spiritual resources. The primary endpoint of this trial is patient reported symptom burden using the Edmonton Symptom Assessment System score. Secondary endpoints are patient reported quality of life using the NCCN survivorship assessment and long-term overall survival. To date, 45 patients have been enrolled. Clinical trial information: NCT05947695 .
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 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".