Choice-point nets: A discrete-event modelling technique for analyzing health care protocols
Bibliographic record
Abstract
Every health care system employs a set of protocols to manage and reduce the impact of infectious disease whenever it appears within the population. Although a great deal of research has been conducted to determine when an outbreak is occurring, research pertaining to whether the response policies are effective is not easily located. Much of the difficulty lies in selecting an appropriate modelling mechanism. To correctly capture a protocol's characteristics, a model must incorporate time and probability, manage large numbers of people and offer analysis that can answer the questions health care administrators will want to ask. Choice-point nets (CNs) are an augmented form of Petri net and offer just such an approach. The enabled transitions in CNs must fire according to their defined timing constraints based on a global clock. Once fired, the outcome of the transition is selected from a set of choices, each of which has a probability attached. Analysis can be performed by unravelling the net into an augmented reachability graph. It is shown how CNs can be employed to analyze outbreak management protocols within a long-term care facility.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".