Challenges in the care of patients with RET-altered thyroid cancer: a multicountry mixed-methods study
Bibliographic record
Abstract
BACKGROUND: The discovery of driver oncogenes for thyroid carcinomas and the identification of genomically targeted therapies to inhibit those oncogenes have altered the treatment algorithm in thyroid cancer (TC), while germline testing for RET mutations has become indicated for patients with a family history of RET gene mutations or hereditary medullary TC (MTC). In the context of an increasing number of selective RET inhibitors approved for use, this paper aims to describe challenges and barriers affecting providers' ability to deliver optimal care for patients with RET-altered TC across the patient healthcare journey. METHODS: A mixed-method educational and behavioral needs assessment was conducted in Germany (GER), Japan (JPN), the United Kingdom (UK), and the United States (US) prior to RET-selective inhibitor approval. Participants included medical oncologists (MO), endocrinologists (EN) and clinical pathologists (CP) caring for patients affected with TC. Data collection tools were implemented in three languages (English, German, Japanese). Qualitative data were coded and thematically analyzed in NVivo. Quantitative data were analyzed via frequency and crosstabulations in SPSS. The findings presented here were part of a broader study that also investigated lung cancer challenges and included pulmonologists. RESULTS: A total of 44 interviews and 378 surveys were completed. Suboptimal knowledge and skills were self-identified among providers, affecting (1) assessment of genetic risk factors (56%, 159/285 of MOs and ENs), (2) selection of appropriate genetic biomarkers (59%, 53/90 of CPs), (3) treatment plan initiation (65%, 173/275 of MOs and ENs), (4) management of side effects associated with multitargeted tyrosine kinase inhibitors (78%, 116/149 of MOs and ENs), and (5) transfer of patients into palliative care services (58%, 160/274 of MOs and ENs). Interviews underscored the presence of systemic barriers affecting the use of RET molecular tests and selective inhibitors, in addition to suboptimal knowledge and skills necessary to manage the safety and efficacy of targeted therapies. CONCLUSION: This study describes concrete educational needs for providers involved in the care of patients with RET-altered thyroid carcinomas. Findings can be used to inform the design of evidence-based education and performance improvement interventions in the field and support integration into practice of newly approved RET-selective inhibitors.
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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 | 0.022 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".