Bibliographic record
Abstract
In Canada, access to magnetic resonance imaging (MRI) examination is limited with an outcome of long patient waiting time. It is reported that the current median waiting time for MRI examination in Saskatoon is almost double the target for waiting time, which may aggravate the disease. This research is towards reducing the waiting time of patients for MRI examination in Canada by applying an improved management. As a first step of this effort, a comprehensive model of MRI booking and serving system is needed. The city of Saskatoon was taken as an example and the MRI booking and serving system in the city was studied. The common tools (queuing theory, system dynamics (SD) and discrete event dynamics simulation (DES)) were compared and it is found that DES is more suitable, in particular Petri nets (PNs), deemed to be the best choice for the purpose of this thesis. The model in this research was constructed on the basis of Hierarchical Coloured Petri nets (HCPNs), a combination of two extended PNs: Coloured PNs (CPNs) and Hierarchical PNs (HPNs). The model is able to simulate and predict patients’ waiting times. Given that the structure of the model developed by HCPNs is still too complex, two extensions to CPNs, Ordered CPNs (OCPNs) and Prioritized HCPNs (PHCPNs), were proposed in this study to reduce the complexity of the model. Validation of the model was performed using the data of Saskatoon Health Region Royal University Hospital. The results have shown that the proposed model can effectively describe the real system. The model has potential applications in decision-making for the selection of an optimal booking strategy to shorten waiting time and in the prediction of possible waiting time of the system in the future, which may assist MRI administrators in the management of medical resources and may greatly improve the quality of MRI service.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".