Patient transfers during hospitalization: An examination of intra facility patient locations using network analysis
Bibliographic record
Abstract
Patient movements following hospitalization are difficult to track. In a large Midwestern academic institution, we analyzed data using network statistics for patients discharged by the hospitalist service between June 2016-June 2018. We retrieved all major patient movements logged in the patient throughput management system following admission. The 4,869 patients discharged by the hospitalist service during the study period experienced 6,832 movements. The mean was 1.4 movements per patient while the maximum was 8. Most patients (72.3%) moved once following hospitalization while 27.7% moved more than once. The predominant movement type was downgrades which comprised 51.8% (n = 3,543) of all movements. Lateral movements were the next most common (25.9%, n = 1,771). Network statistics revealed progressive care units to be central to patient flow across the system. Transfers following hospitalization are common. Visualizing these transfers using network statistics may provide valuable insights to enhance patient safety.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".