Five years of EMA-approved systemic cancer therapies for solid tumours—a comparison of two thresholds for meaningful clinical benefit
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Several societies have proposed frameworks to evaluate the benefit of oncology drugs; one prominent tool is the European Society for Medical Oncology Magnitude of Clinical Benefit Scale (ESMO-MCBS). Our objectives were to investigate the extent of European Medicines Agency (EMA)-approved cancer drugs that meet the threshold for 'meaningful clinical benefit' (MCB), defined by the framework, and determine the change in the distribution of grades when an adapted version that addresses the scale's limitations is applied. METHODS: We identified cancer drugs approved by the EMA (2011-2016). We previously proposed adaptations to the ESMO-MCBS addressing its main limitations, including the use of the lower limit of the 95% confidence interval in assessing the hazard ratio. To assess the MCB, both the original and adapted ESMO-MCBS were applied to the respective approval studies. RESULTS: In total, we identified 70 approval studies for 38 solid cancer drugs. 21% of therapies met the MCB threshold by the original ESMO-MCBS criteria. In contrast, only 11% of therapies met the threshold for MCB when the adapted ESMO-MCBS was applied. Thus 89% and 79% of therapies did not meet the MCB threshold in the adapted and original ESMO-MCBS, respectively. CONCLUSIONS: In most of the cancer drugs, the MCB threshold is not met at the time of approval when measured using both ESMO-MCBS scales. Since approval status does not translate into a MCB, stakeholders and decision makers should focus on the benefit/risk ratio of anticancer drugs to assure an appropriate allocation of resources in health care systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it