Proposal of a Method for Transferring High-Quality Scientific Literature Data to Virtual Patient Cases Using Categorical Data Generated by Bernoulli-Distributed Random Values: Development and Prototypical Implementation
Bibliographic record
Abstract
BACKGROUND: Teaching medicine is a complex task because medical teachers are also involved in clinical practice and research and the availability of cases with rare diseases is very restricted. Automatic creation of virtual patient cases would be a great benefit, saving time and providing a wider choice of virtual patient cases for student training. OBJECTIVE: This study explored whether the medical literature provides usable quantifiable information on rare diseases. The study implemented a computerized method that simulates basic clinical patient cases utilizing probabilities of symptom occurrence for a disease. METHODS: Medical literature was searched for suitable rare diseases and the required information on the respective probabilities of specific symptoms. We developed a statistical script that delivers basic virtual patient cases with random symptom complexes generated by Bernoulli experiments, according to probabilities reported in the literature. The number of runs and thus the number of patient cases generated are arbitrary. RESULTS: We illustrated the function of our generator with the exemplary diagnosis "brain abscess" with the related symptoms "headache, mental status change, focal neurologic deficit, fever, seizure, nausea and vomiting, nuchal rigidity, and papilledema" and the respective probabilities from the literature. With a growing number of repetitions of the Bernoulli experiment, the relative frequencies of occurrence increasingly converged with the probabilities from the literature. For example, the relative frequency for headache after 10.000 repetitions was 0.7267 and, after rounding, equaled the mean value of the probability range of 0.73 reported in the literature. The same applied to the other symptoms. CONCLUSIONS: The medical literature provides specific information on characteristics of rare diseases that can be transferred to probabilities. The results of our computerized method suggest that automated creation of virtual patient cases based on these probabilities is possible. With additional information provided in the literature, an extension of the generator can be implemented in further research.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".