Precision Medicine: Academic dreaming or clinical reality?
Bibliographic record
Abstract
Precision medicine can be distilled into a concept of accounting for an individual's unique collection of clinical, physiologic, genetic, and sociodemographic characteristics to provide patient-level predictions of disease course and response to therapy. Abundant evidence now allows us to determine how an average person with epilepsy will respond to specific medical and surgical treatments. This is useful, but not readily applicable to an individual patient. This has brought into sharp focus the desire for a more individualized approach through which we counsel people based on individual characteristics, as opposed to population-level data. We are now accruing data at unprecedented rates, allowing us to convert this ideal into reality. In addition, we have access to growing volumes of administrative and electronic health records data, biometric, imaging, genetics data, microbiome, and other "omics" data, thus paving the way toward phenome-wide association studies and "the epidemiology of one." Despite this, there are many challenges ahead. The collating, integrating, and storing sensitive multimodal data for advanced analytics remains difficult as patient consent and data security issues increase in complexity. Agreement on many aspects of epilepsy remains imperfect, rendering models sensitive to misclassification due to a lack of "ground truth." Even with existing data, advanced analytics models are prone to overfitting and often failure to generalize externally. Finally, uptake by clinicians is often hindered by opaque, "black box" algorithms. Systematic approaches to data collection and model generation, and an emphasis on education to promote uptake and knowledge translation, are required to propel epilepsy-based precision medicine from the realm of the theoretical into routine clinical practice.
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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 | 0.003 | 0.317 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".