Special Series on Comparative Effectiveness Research: Challenges to Real-World Solutions to Quality Improvement in Personalized Medicine
Bibliographic record
Abstract
McShane and Hayes and Ginsburg and Kuderer in the special series issue of the Journal provide a comprehensive vision for improved conduct and reporting of predictive/prognostic biomarker research in personalized cancer medicine. Their visions are prompted by the need for more methodologic rigor for validation studies in this field as highlighted by a critical appraisal of 37 published reports in a systematic review. They invoke three strategies for improving study quality: first, comparative effectiveness research (CER) that addresses population-level effects of alternative interventions; second, quality improvement (QI) tools as methodologic standards; and third, infrastructure development for informatics and data linkage, and institutional networking with data sharing.
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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 | 0.075 | 0.200 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.073 | 0.068 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".