Judging quality and coordination in biomarker diagnostic development
Bibliographic record
Abstract
What makes a high-quality biomarker experiment? The success of personalized medicine hinges on the answer to this question. Unfortunately, as many commentators have now emphasized, the quality of most biomarker experiments to date has been quite low. Although the technical side of this problem has received considerable attention, the philosophical issues remain largely unexplored. In this paper, I argue that understanding what constitutes a high-quality biomarker experiment requires some fundamental shifts in how we think about the epistemology, ontology, and methodology of clinical translation.; ¿Qué convierte a un experimento con biomarcadores en un experimento de gran calidad? El éxito de la medicina personalizada depende de la respuesta a esta pregunta. En este artículo sostengo que el juicio sobre la calidad de los experimentos con biomarcadores está mediado por el problema de la subdeterminación teórica, es decir, la red de teorías biológicas y patofisiológicas que motivan un experimento con biomarcadores es lo bastante complicada como para frustrar a menudo una interpretación válida de los resultados experimentales. A partir de un caso de desarrollo de diagnóstico con biomarcadores, defiendo que el problema de la subdeterminación puede ser superado con mayor coordinación en la trayectoria de investigación sobre el biomarcador. Después sugiero un enfoque para evaluar la coordinación a lo largo de una trayectoria de investigación. Por último concluyo que lo que hace que un experimento con biomarcadores tenga una alta calidad debe dirimirse en función de la contribución epistémica que aquél realiza sobre este esfuerzo investigador coordinado.
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.
How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".