Choice-point nets: A discrete-event modelling technique for analyzing health care protocols
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it