Bibliographic record
Abstract
Efficient management of patient admissions plays a critical role in increasing a hospital's \nresource utilization and reducing health care costs. We consider the problem of fi nding the \nbest available admission policy for elective hospital admissions under real time constraints. \nThe problem is modeled as a Markov Decision Process (MDP) and we investigate current \nstate-of-the art real time planning methods. \n \nDue to the complexity of the model, traditional mode-based planners are limited in scalability \nsince they require an explicit enumeration of the model dynamics. To overcome this challenge, \nwe apply sample-based planners along with efficient simulation techniques that given an \ninitial start state, generate an action on-demand while avoiding portions of the model \nthat are irrelevant to the start state. \n \nResults show that given reasonable resources, our approach generates improved deci- \nsions over existing alternatives that fail to scale as model complexity increases. We also \npropose a parameter tuning method that can be easily and efficiently implemented.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".