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
While both prognostic and predictive cancer biomarkers predict clinical outcome, the term 'predictive biomarker' is reserved for the association of a specific therapy with a specific clinical outcome. The advent of genomic signatures and next generation sequencing as candidate predictive biomarkers has led to lengthy and expensive processes for biomarker qualification. The urgency to bring novel predictive cancer biomarkers to practice faster and cheaper requires strategies to lower the bar to biomarker implementation. Three strategies are suggested: identify biomarkers closely coupled to biologic mechanism associated with the clinical endpoint and scalable from cells to humans; identify biomarkers that can be reliably detected and quantified; and assess biomarkers by capacity to reduce toxicity as well as to increase therapy efficacy. Biomarker selection directly and closely related to production of end points by biologic mechanism demonstrated by a ladder of evidence should require less burden of proof clinically than biomarkers that are merely associative.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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