Population kinetics of progression free survival (PFS).
Bibliographic record
Abstract
e18251 Background: We assessed drug type impact on whether PFS curves could be fit by 2 phase decay models on nonlinear regression analysis (NLRA). Methods: We digitized 894 published PFS curves for incurable cancers. We used GraphPad Prism 7 for 1 phase and 2 phase decay NLRA, with constraints Y0 = 100 and plateau = 0. We defined curves as fitting 2 phase models if each subpopulation was ≥1% of the entire population and if subpopulation half-lives differed by a factor of ≥2, or if log-linear plots demonstrated unequivocal 2 phase decay. Results: PFS curves for single agents showed either high (≥75%) or low ( < 30%) probability of 2 phase decay, depending on drug type (p < 0.0001, Table). 11/11 PD1/ipilimumab combinations had 2 phase decay vs 36/209 curves (17%) for all other combinations. Conclusions: Drugs have either high or low probability of PFS curve 2 phase decay. Clinical trial methods or some mechanisms of acquired resistance might contribute to 2 phase decay, but 2 phase decay also could indicate a dichotomous factor (eg gene mutation/deletion or complete pathway silencing) producing 2 distinct subpopulations with differing progression rates. Drugs with high 2 phase decay could be prime candidates for RNA & whole genome sequencing, pathway expression studies etc to identify dichotomous predictive factors. Further assessment is needed to better understand why some drugs behave differently when given in combinations vs as single agents. [Table: see text]
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".