Establishing an ambulance dispatch system for intrahospital transfers in a large teaching hospital in India
Bibliographic record
Abstract
During the Covid Pandemic, a lot of structural and process changes had to be made in a quick time in almost all the hospitals to accommodate the patients and admit them with the least exposure to the Hospital Staff and the bystanders of the patients. AIIMS Hospital in New Delhi India is a premier tertiary care teaching hospital, which is spread out in different areas. Two Hospital centers of AIIMS were designated as COVID Hospitals. Since there was no previous experience of intrahospital transfers of this magnitude, the hospital had to face lots of difficulties in such transfers and this translated into increased turnaround time. This paper concentrates on the mechanisms in which the Department of Hospital Administration found out the various issues plaguing this process. Later by Change Management, an Intervention was brought in, which helped in the framing of a standard operating procedure that helped in the easy transfer of the patients which was hassle-free and which continued to the second wave of the COVID pandemic.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".