Effectiveness and Cost-Effectiveness of Survivorship Care for Survivors of Hodgkin Lymphoma (INSIGHT Study): Protocol for a Multicenter Retrospective Cohort Study With a Quasi-Experimental Design
Bibliographic record
Abstract
BACKGROUND: Hodgkin lymphoma (HL) occurs at young ages, with the highest incidence between 20 and 40 years. While cure rates have improved to 80%-90% over the past decades, survivors of HL are at substantial risk of late treatment-related complications, such as cardiovascular diseases, breast cancer, severe infections, and hypothyroidism. To reduce morbidity and mortality from late treatment effects, the Dutch Better care after lymphoma, Evaluation of long-term Treatment Effects and screening Recommendations (BETER) consortium developed a survivorship care program for 5-year survivors of HL that includes risk-based screening for and treatment of (risk factors for) late adverse events. Even though several cancer survivorship care programs have been established worldwide, there is a lack of knowledge about their effectiveness in clinical practice. OBJECTIVE: The Improving Nationwide Survivorship care Infrastructure and Guidelines after Hodgkin lymphoma Treatment (INSIGHT) study evaluates whether Dutch BETER survivorship care for survivors of HL decreases survivors' burden of disease from late adverse events after HL treatment and associated health care costs and improves their quality of life. METHODS: The INSIGHT study is a multicenter retrospective cohort study with a quasi-experimental design and prospective follow-up, embedded in the national BETER survivorship care infrastructure. The first BETER clinics started in 2013-2016 and several other centers started or will start BETER clinics in 2019-2024. This allows us to compare survivors who did and those who did not receive BETER survivorship care in the last decade. Survivors in the intervention group are matched to controls (n=450 per group) based on sex, age at diagnosis (±5 years), age in 2013 (±5 years), and treatment characteristics. The primary outcome is the burden of disease in disability-adjusted life years from cardiovascular disease, breast cancer, severe infections, and hypothyroidism. In a cost-effectiveness analysis, we will assess the cost of BETER survivorship care per averted or gained disability-adjusted life year and quality-adjusted life year. Secondary outcomes are BETER clinic attendance, adherence to screening guidelines, and knowledge and distress about late effects among survivors of HL. Study data are collected from a survivor survey, a general practitioner survey, medical records, and through linkages with national disease registries. RESULTS: The study was funded in November 2020 and approved by the institutional review board of the Netherlands Cancer Institute in July 2021. We expect to finalize recruitment by October 2024, data collection by early 2025, and data analysis by May 2025. CONCLUSIONS: INSIGHT is the first evaluation of a comprehensive survivorship program using real-world data; it will result in new information on the (cost-)effectiveness of survivorship care in survivors of HL in clinical practice. The results of this study will be used to improve the BETER program where necessary and contribute to more effective evidence-based long-term survivorship care for lymphoma survivors. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/55601.
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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 | 0.052 | 0.046 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.042 | 0.005 |
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".