Bibliographic record
Abstract
Emergency health care should be provided as carefully and efficiently as possible. We consider patients in a city who are in an emergency condition and need to be distributed across the city's hospitals using an ambulance service$\mathcal{A}$. Patients are characterized by the type of care they need and the city's area where they are located. The costs of hospitalization and transportation of a patient obviously depend on these two characterization elements. We present a method to optimally distribute patients, i.e. we choose which hospital each patient is sent to so that hospital and ambulance capacities are not exceeded and the total cost to be paid to$\mathcal{A}$and hospitals is minimal. A pricing model is developed to determine payments to hospitals and$\mathcal{A}$. Machine learning is suggested to predict the number of patients in each city's area. The management of our optimal patient distribution is carried out using a publicly reliable smart contract in blockchain. This ensures that patient transport and hospitalization, as well as corresponding payments, are recorded in blockchain in a secure, immutable, transparent and decentralized manner. Finally, we suggest how to make patient distribution robust by handling faulty behaviors of hospitals and$\mathbf{A}$and imperfect patient number predictions (PNPs) in city's areas. To this end, we develop a method to detect faulty behaviors and compute corresponding financial penalties, which is particularly difficult with imperfect PNPs.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".