Evaluating the Implementation of Integrated Proactive Supportive Care Pathways in Oncology: Master Protocol for a Cohort Study
Bibliographic record
Abstract
BACKGROUND: Supportive care (SC) refers to the prevention and management of complications of cancer and its treatment. While it has long been recognized as an important cancer care delivery component, a high proportion of patients face unaddressed SC needs, calling for innovative approaches to deliver SC. OBJECTIVE: The objective of this master protocol is to evaluate the implementation of different integrated proactive SC pathways across the cancer care continuum in our institution (Gustave Roussy, Villejuif, France). Pathways studied in this master protocol may occur shortly after diagnosis to prevent treatment-related burden; during treatment to monitor the onset of toxicities and provide timely symptom management; and after treatment to improve rehabilitation, self-management skills, and social reintegration. METHODS: This study is guided by the Reach, Effectiveness, Adoption, Implementation, and Maintenance framework. The primary objective is to evaluate the impact of SC pathways on patients' distress and unmet needs after 12 weeks, measured by the National Comprehensive Cancer Network's Distress Thermometer and Problem List. Secondary objectives will focus on the pathways (macrolevel) and each SC intervention (microlevel), evaluating their reach (administrative data review of the absolute number and proportion of clinical and sociodemographic characteristics of patients included in the pathways); short-term and long-term efficacy through their impact on quality of life (EQ-5D-5L and the 30-item European Organization for Research and Treatment of Cancer Quality of Life Core Questionnaire) and symptom burden (MD Anderson Symptom Inventory, Hospital Anxiety and Depression Scale, Insomnia Severity Index, and 22-item European Organization for Research and Treatment of Cancer Sexual Health Questionnaire); adoption by patients and providers (administrative data review of SC referrals and attendance or use of SC strategies); barriers to and leverage for implementation (surveys and focus groups with patients, providers, and the hospital organization); and maintenance (cost-consequence analysis). Pilot evaluations with a minimum of 70 patients per pathway will be performed to generate mean Distress Thermometer scores and SDs informing the calculation of formal sample size needed for efficacy evaluation (cohorts will be enriched accordingly). RESULTS: The study was approved by the ethics committee, and as of February 2024, a total of 12 patients were enrolled. CONCLUSIONS: This study will contribute toward innovative models of SC delivery and will inform the implementation of integrated SC pathways of care. TRIAL REGISTRATION: ClinicalTrials.gov NCT06479057; https://clinicaltrials.gov/study/NCT06479057. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/52841.
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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 | 0.103 | 0.095 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".