Relocation and transfer of patients to a new hospital: Practical lessons
Bibliographic record
Abstract
Objective: We describe the practical aspects of planning for and executing the safe movement of patients and care teams from an existing tertiary hospital (Mafraq Hospital) to a new hospital (Sheikh Shakhbout Medical City) in Abu Dhabi, United Arab Emirates.Methods: Field notes and measures taken during the planning and execution of this event were prospectively collated by the authors to inform the final manuscript.Results: A central command structure similar to that used for major disaster management helped to guide the move of all inpatients, staff and support services from one hospital to the other. Five patient tracks (clinical teams) were established to move patients to the new facility concurrently along set and separate routes. Five additional support tracks were established to provide logistical support for the movement of essential non-patient resources. A total of 142 acutely ill general care and critically ill hospital patients were moved during a five-hour period with zero patient harm events.Conclusions: The tools, processes used, and lessons learned in this exercise are shared in the hope that others who are required to move hospitals can learn from and use our experience.
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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 | 0.021 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".