Overcoming the translational roadblocks: a cancer care and research model
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
There are many challenges to the process of translating the knowledge gained in the laboratory into new clinical approaches that can meet the needs of patients, clinicians and the wider community. We describe here an initiative that has borrowed concepts and principles from participatory research to produce a new process embedded in a cancer center aiming to facilitate translational research and overcome the three translational roadblocks. The centre-wide project named Personal Response Determinants in Cancer Therapy (PREDICT) operates with the support of the centre's leadership, staff, volunteers and patients to contribute to current and future cancer research successes. We describe the different phases of the project, the current structure and lessons learned during its evolution, highlighting how PREDICT contributes to translational research and its linkage to participatory research concepts. Despite the contextualized nature of the PREDICT initiative, we believe that the framework developed for the project has the potential to help other clinical centers to overcome the translational research roadblocks.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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