Rare versus common diseases: a false dichotomy in precision medicine
Bibliographic record
Abstract
Precision medicine initiatives are being launched worldwide, each with the capacity to sequence many thousands to millions of human genomes. At the strategic planning level, all are debating the extent to which these resources will be directed towards rare diseases (and cancers) versus common diseases. However, these are not mutually exclusive choices. The organizational and governmental infrastructure created for rare diseases is extensible to common diseases. As we will explain, the underlying technology can also be used to identify drug targets for common diseases with a strategy focused on naturally occurring human knockouts. This flips on its head the prevailing modus operandi of studying people with diseases of interest, shifting the onus to defining traits worth emulating by pharmaceuticals, and searching phenotypically for people with these traits. This also shifts the question of what is rare or common from the many underlying causes to the possibility of a common final pathway.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".