Technology-enhanced palliative care for patients with cancer on phase 1 clinical trials.
Bibliographic record
Abstract
TPS12136 Background: Patients w advanced cancer participating in Phase I trials carry a high symptom burden from cancer and prior therapies. Our prior work shows patients on phase I trials w multiple active symptoms impacting their immediate quality of life with implications on toxicities and clinical outcomes on subsequent therapy. To identify an effective scalable approach to comprehensive symptom management for patients w adv cancer on phase I trials, we leveraged the increased technology use to design a technology-enhanced symptom management and palliative care intervention (TEC). Methods: Patients w adv cancer seen in the phase I clinic will be given the Edmonton Symptom Assessment System (ESAS), a validated patient-reported outcomes (PRO) tool of common cancer symptoms to identify those with a high symptom burden defined as ≥4 out of 10 on >1 ESAS symptom and a Global Distress Score (GDS) of ≥20. The GDS, a validated score of overall symptom intensity derived from the ESAS, is comprised of 6 physical (pain, fatigue, nausea, drowsiness, appetite, shortness of breath) & 2 psychosocial symptoms (depression, anxiety), and overall wellbeing. TEC is an innovative patient-centered care program of strategic vigorous symptom management where standard-of-care clinic visits are complemented by proactive symptom monitoring between clinic visits remotely and through provider-initiated calls. In this pilot randomized study, we will determine the effect sizes of High-Intensity TEC (HI-TEC; q3day remote PRO assessments w preset provider-initiated call bw visits), Low-Intensity TEC (LO-TEC; q5day remote PRO assessments w preset provider-initiated call bw visits), and Standard Palliative Care (no preset provider contact bw visits). Our guiding hypothesis is that a comprehensive, proactive, technology-enhanced symptom management program led by a Palliative Care team can mitigate the high symptom burden of patients with advanced cancers enrolling in phase I trials. The primary objective assesses the effect size of each TEC intervention on the GDS measure of symptom burden prior to C1D1 on phase I trial. Our working hypothesis is that HI-TEC and LO-TEC will be associated with a lower overall symptom burden signifying symptom optimization prior to starting on a phase I trial. Secondary objectives aim to estimate the effect size of TEC on the following: Symptom burden over 12 weeks on a phase I trial using ESAS, quality of life using FACIT-Sp, PRO-CTCAE and patient satisfaction using FAMCARE-P13. clinical outcomes at 6 months including OS, treatment outcomes (interruptions, dose reductions, discontinuation, time on trial) and quality metrics for end-of-life (EOL) (chemotherapy in the last 14 days of life, ICU admit in last 30 days of life, death without hospice or < 3d of hospice). Qualitatively assessment of patients’ + caregivers’ perceptions of receiving TEC-based cancer care. Clinical trial information: NCI-2020-07465.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".