Appraising the holistic value of Lenvatinib for radio-iodine refractory differentiated thyroid cancer: A multi-country study applying pragmatic MCDA
Bibliographic record
Abstract
BACKGROUND: The objective of the study was to reveal through pragmatic MCDA (EVIDEM) the contribution of a broad range of criteria to the value of the orphan drug lenvatinib for radioiodine refractory differentiated thyroid cancer (RR-DTC) in country-specific contexts. METHODS: The study was designed to enable comprehensive appraisal (12 quantitative, 7 qualitative criteria) in the current disease context (watchful waiting, sorafenib) of France, Italy and Spain. Data on the value of lenvatinib was collected from diverse stakeholders during country-specific panels and included: criteria weights (individual and social values); performance scores (judgments on evidence-collected through MCDA systematic review); qualitative impacts of contextual criteria; and verbal and written insights structured by criteria. The value contribution of each criterion was calculated and uncertainty explored. RESULTS: Comparative effectiveness, Quality of evidence (Spain and Italy) and Disease severity (France) received the greatest weights. Four criteria contributed most to the value of lenvatinib, reflecting its superior Comparative effectiveness (16-22% of value), the severity of RR-DTC (16-22%), significant unmet needs (14-21%) and robust evidence (14-20%). Contributions varied by comparator, country and individuals, highlighting the importance of context and consultation. Results were reproducible at the group level. Impacts of contextual criteria varied across countries reflecting different health systems and cultural backgrounds. The MCDA process promoted sharing stakeholders' knowledge on lenvatinib and insights on context. CONCLUSIONS: The value of lenvatinib was consistently positive across diverse therapeutic contexts. MCDA identified the aspects contributing most to value, revealed rich contextual insights, and helped participants express and explicitly tackle ethical trade-offs inherent to balanced appraisal and decisionmaking.
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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 | 0.082 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".