Clinical benefit of cancer drugs approved in Switzerland 2010–2019
Bibliographic record
Abstract
BACKGROUND: It is unknown to what extent cancer drugs approved in Switzerland by the Swissmedic fulfil criteria of clinical benefit according to the European Society of Medical Oncology Magnitude of Clinical Benefit Scale version 1.1 (ESMO-MCBS), the American Society of Clinical Oncology Value Framework version 2 (ASCO-VF) and the Swiss OLUtool v2 (OLUtool). PATIENTS AND METHODS: An electronic search identified studies that led to marketing authorisations in Switzerland 2010-2019. Studies were evaluated according to ESMO-MCBS, ASCO-VF and OLUtool. Substantial benefit for ESMO-MCBS, was defined as a grade A or B for (neo)adjuvant intent and 4 or 5 for palliative intent. For ASCO-VF and OLUtool clinical benefit was defined as score ≥45 and A or B, respectively. Concordance between the frameworks was calculated with Cohen's Kappa (κ). Factors associated with clinical benefit were evaluated by logistic regression. RESULTS: In the study period, 48 drugs were approved for 92 evaluable indications, based on 100 studies. Ratings for ESMO-MCBS, ASCO-VF and OLUtool could be performed for 100, 86, and 97 studies, respectively. Overall, 39 (39%), 44 (51%), 45 (46%) of the studies showed substantial clinical benefit according to ESMO-MCBS v1.1, ASCO-VF, OLUtool criteria, respectively. There was fair concordance between ESMO-MCBS and ASCO-VF in the palliative setting (κ = 0.31, P = 0.004) and moderate concordance between ESMO-MCBS and OLUtool (κ = 0.41, P<0.001). There was no significant concordance between ASCO-VF and OLUtool (κ = 0.18, P = 0.12). Factors associated with substantial clinical benefit in multivariable analysis were HRQoL benefit reported as secondary outcome for ESMO-MCBS and the ASCO-VF and blinded studies for OLUtool. CONCLUSIONS: At the time of approval, only around half of the trials supporting marketing authorisation of recently approved cancer drugs in Switzerland meet the criteria for substantial clinical benefit when evaluated with ESMO-MCBS, ASCO-VF or OLUtool. There was at best only moderate concordance between the grading systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".