Bibliographic record
Abstract
BACKGROUND: Health care decisions are often made under uncertainty and modeling is used to inform the choices and possible consequences. State-transition ("Markov") models are commonly used but they represent the problem solely in terms of states; events are not explicitly considered. METHODS: Discretely integrated condition event (DICE) simulation provides for both aspects that persist over time ("conditions") and for those happening at a point in time ("events"). A Markov model can be specified in DICE by representing states as conditions with a recurrent transition event processing transition probabilities, and other events added explicitly. RESULTS: The DICE specification of a Markov model is compact because transitions are enumerated only once; it is very transparent, as these specifications are tabulated rather than programmed in code; and flexibility is enhanced by the ease with which alternative structures are specified. Events can be added to represent clinical occurrences, treatment features, health care activities, and any other relevant aspect of this type. They may coincide with the transition event or occur at their own times. Varying cycle times and structural sensitivity analyses are easy to implement. LIMITATIONS: Execution of a DICE simulation using a macro in spreadsheet software can be slow, especially for complex models requiring stochastic analyses replicated thousands of times. Modelers wishing to use other software can still use the tabular specification ideas to expand their Markov models, but the descriptions provided here may not be entirely applicable. Another limitation is the inability of these simulations to handle constrained resources or interactions among patients. CONCLUSIONS: With DICE simulation, it is possible to expand the Markov formulation to include explicitly many events occurring at various times.
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 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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".