Irreversible treatment decisions under consideration of the research and development pipeline for new therapies
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
This article addresses a topic not considered in previous models of patient treatment: the possible downstream availability of improved treatment options coming out of the medical research and development (R&D) pipeline. We provide clinical examples in which a patient may prefer to wait and take the chance that an improved therapy comes to market rather than choose an irreversible treatment option that has serious quality of life ramifications and would render future treatment discoveries meaningless for that patient. We then develop a Markov decision process model of the optimal time to initiate treatment, which incorporates uncertainty around the development of new therapies and their effects. After deriving structural properties for the model, we provide a numerical example that demonstrates how models that do not have any foresight of the R&D pipeline may result in optimal policies that differ from models that have such foresight, implying erroneous decisions in the former models. Our example quantifies the effects of such errors.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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