Bibliographic record
Abstract
Phase II clinical trials have long been used to screen new cancer therapeutics for antitumor activity ("efficacy") worthy of further evaluation. Traditionally, the primary end point used in these screening trials has been objective response rate (RR), with the desired rate being arbitrarily set by the researchers before initiation of the trial. For cytotoxic agents, especially in common tumor types, response has been a reasonably robust and validated surrogate of benefit. Phase II trials with response as an end point have a modest sample size (15-40 patients) and are completed rapidly allowing early decisions regarding future development of a given agent. More recently, a number of new agents have proven successful in pivotal phase III studies, despite a low or very modest RR demonstrated in early clinical trials. Researchers have postulated that these novel agents, as a class, may not induce significant regression of tumors, and that the use of RR as an end point for phase II studies will result in false negative results, and point out that not all available data is used in making the decision. Others have pointed out that even novel agents have proven unsuccessful in pivotal trials if objective responses are not demonstrated in early clinical trials. We review here the historical and current information regarding objective tumor response.
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 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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".