A randomized study to measure and enhance the health-related quality of life in patients with cancer receiving immune checkpoint modulators (ME-Q).
Bibliographic record
Abstract
TPS6603 Background: Existing data demonstrate the impact of Immune checkpoint modulators (ICMs) on Health-Related Quality of Life (HRQOL), but this was from studies that used HRQOL tools developed for patients treated with chemotherapy and/or radiation. To address this gap, we developed a toxicity subscale to cover important immune-related adverse events (irAEs) to combine this subscale with the generic FACT-G to measure HRQOL in patients treated with ICMs, the FACT-ICM. This trial proposes to validate the use of the FACT-ICM as a tool for HRQOL measurement and simultaneously to assess the benefit of remote symptom monitoring and management using a new electronic platform developed specifically for patients receiving ICMs. Methods: Participants will be randomized (1:1) before starting their standard of care ICM therapy to either usual care or the monitoring arm. The ME-Q platform uses a web-based application to enable remote monitoring of patient symptoms and HRQOL, allowing all participants enrolled in both the usual care or monitoring arms to complete the FACT-ICM and other questionnaires (DART, Health Resource Utilization, Decision regret scale, post-study system usability questionnaire) at set time points. Patients on the monitoring arm will have their electronic responses sent automatically, and new or significant worsening symptoms of clinical concern will generate an alert to the advanced practicing nurse (APN) responsible for replying to the patient and acting on responses. Via phone or a video teleconference, the nurse will perform a targeted assessment and provide standardized clinical advice and interventions based on well-established clinical algorithms and international guidelines for the management of irAEs. The APN will inform the patient's clinical team about the patient's responses and obtain the treating team's input and agreement on a proposed treatment plan. Patients enrolled in the usual care arm will respond to the questionnaires; however, their responses are not transmitted to their clinical team. The primary objective will be to validate the PRO tool for HRQOL measurement, and the secondary objective is to improve HRQOL in cancer patients receiving ICMs by remote monitoring and symptom management using the ME-Q electronic platform. We estimate that a total sample size of 266 patients (divided into two arms) achieves 90% power to detect an effect size of 0.4 in mean score changes (from baseline to 4 months) between arms using a two-sample t-test with a two-sided significance level of 0.05. Enrolment is to be completed in 2 years.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".