P4 Medicine or O4 Medicine? Hippocrates Provides the Answer
Bibliographic record
Abstract
BACKGROUND: The term P4 medicine (predictive, preventative, personalized, participatory) was coined by Dr. Leroy Hood of the Institute for Systems Biology to demonstrate his framework to detect and prevent disease through extensive biomarker testing, close monitoring, deep statistical analysis, and patient health coaching. METHODS: In 2017, this group published the results of their "100 Person Wellness Project." They performed whole genome sequencing and 218 clinical laboratory tests, measured 643 metabolites and 262 proteins, quantified 4616 operational taxonomic units in the microbiome, and monitored exercise in 108 participants for 9 months. The study was also interventional, as members were paired with a coach who gave lifestyle and supplement counseling to improve biomarker levels between each sampling period. RESULTS: Using this study as a basis, we here analyze the Hippocratic roots and the advantages and disadvantages of P4 medicine. We introduce O4 medicine (overtesting, overdiagnosis, overtreatment, overcharging) as a counterpoint to P4 medicine to highlight the drawbacks, including possible harms and cost. CONCLUSIONS: We hope this analysis will contribute to the discussion about the best use of limited health-care resources to produce maximum benefit for all patients.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".