Value-Added Decisions in Oncology
Bibliographic record
Abstract
Registration of new anticancer drugs is decided too often not by their clinical value but by a p value. Approval is granted if the difference in an acceptable time-to-event outcome measure differs between experimental and control arms of a randomized controlled trial, such that the null hypothesis can be rejected based on a statistical test that meets the arbitrary criterion of p < .05. However, as stated by the American Statistical Association, a p value does not measure the size of an effect or the importance of a result; it does not provide a good measure of evidence related to a hypothesis, and policy decisions should not be made on the basis of whether a p value passes a specific threshold. Unfortunately, this statement is ignored by most journals, which emphasize p values in reporting results of clinical trials, and by regulatory agencies, such as the U.S. Food and Drug Administration and the European Medicines Agency; a significant p value is often a necessary and sufficient criterion for granting marketing approval. As a result, pharmaceutical companies often design large trials to increase the probability that a small difference in the primary outcome measure will be "significant." Moreover, the market price set for such drugs bears no relationship to the level of their benefit; drugs with small effects on outcome are sold at roughly the same price as "good drugs" that convey substantial benefit.
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.
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 | 0.016 | 0.237 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.005 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".