Bibliographic record
Abstract
The practice of medicine has evolved from “arm-chair medicine” through “evidence-based medicine” to “precision medicine”. Medical literature has seen a proliferation of the use of the phrases “precision medicine” and “personalized medicine,” with little distinction made between the two. While both strategies promote individualizing patient care, precision medicine is guided by information based on the genes, proteins, metabolites, and other biomarkers in the human body. In addition to these biological markers, personalized medicine would consider various social, economic, behavioural, and environmental factors that might be specific to a particular individual in planning a treatment strategy unique to that individual. The term P4 Medicine (Predictive, Preventive, Personalized and Participatory) has also been proposed to reflect the increased understanding and implications of the pathobiology of disease on management strategies.1 The use of biologics and cell-based therapies, particularly in cancer therapeutics, has demonstrated the power of these strategies.2 This brief review will focus on how this strategy is currently being applied in the management of severe asthma.
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 | 0.000 | 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 | 0.000 | 0.000 |
| 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".