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
To improve processes in hospitals, a case study was conducted at a German university hospital, which consists of more than 30 clinics and institutes on a large campus. The case study focused on the analysis of the patient flow and of machine utilization in the radiology department. Radiological devices are spread over the campus and located in different buildings. Patients with restricted mobility have to be transported by a vehicle transportation service across the campus. However, a vehicle transport can considerably influence the patients' punctual arrival to their appointments. The observations of the case study showed that the current organization of the radiology department results in high patient waiting times and machine idle times. The university hospital is planning to conduct significant organizational changes, especially with regard to the organization of the radiology department. This case study was conducted to support the planning processes of the clinic and to reveal and estimate optimization potential. To analyze the patient flow, a discrete event multicriteria simulation model was designed and implemented. By modeling different scenarios, it was possible to easily compare and assess distinct alternatives. This led to improved machine utilization and reduced waiting times.
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 | 0.003 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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