‘Overcoming the Bottleneck’: Knowledge Architectures for Genomic Data Interpretation in Oncology
Bibliographic record
Abstract
Abstract In recent years, oncology transitioned from its traditional, organ-based approach to ‘precision oncology’ centered on molecular alterations. As a result, it has become to a significant extent a ‘data-centric’ domain. Its practices increasingly rely on a sophisticated techno-scientific infrastructure that generates massive amounts of data in need of consistent, appropriate interpretations. Attempts to overcome the interpretation bottleneck have led to the establishment of a complex landscape of interrelated resources that, while displaying distinct characteristics and design choices, also entertain horizontal and vertical relations. Although there is no denying that the data-centric nature of contemporary oncology raises a number of key issues related to the production and circulation of data, we suggest that the focus on data use and re-use should be complemented by a focus on interpretation. Oncology practitioners refer to data interpretation resources as ‘knowledgebases’, an actor’s category designed to differentiate them from generic, multi-purpose databases. Their major purpose is the definition and identification of clinically actionable alterations. A heavy investment in human curation, of a clinical rather than exclusively scientific nature is needed to make them valuable, but each knowledgebase appears to have its own peculiar way of connecting clinical and scientific statements. In spite of their common goal, knowledgebases thus adopt very different approaches partly captured by the tension between trust and traceability.
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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 | 0.000 | 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 | 0.001 | 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 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".