Overcoming the translational roadblocks: a cancer care and research model
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.
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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 | 0.156 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.015 | 0.095 |
| Scholarly communication | 0.030 | 0.028 |
| Open science | 0.010 | 0.024 |
| Research integrity | 0.015 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".