The Results of the Expert Survey for the Implementation of the Triage System of Patients – UTAS in the RRCEM and Its Branches
Bibliographic record
Abstract
It is necessary to introduce an emergency triage tool in order to quickly determine the clinical priorities of the increasing Uzbek emergency patients, perform appropriate treatment, and efficiently allocate emergency medical resources such as manpower, facilities, and equipments. Various types of 5-level triage tools are used worldwide, and consultants reviewed the most widely used tools such as MTS (Manchester Triage System), ESI (Emergency Severity Index), CTAS (Canadian Triage and Acuity Scale), and SATS (South African Triage Scale). After discussions with Republican Research Centre of Emergency medicine (RRCEM) experts, it was concluded that among these triage tools, CTAS was most suitable for the Uzbek emergency medical environment. Inadequate triage, lack of beds, spaces, and staffing were pointed out as problems through RRCEM statistics and on-site visits. For the stable introduction and settlement of the UTAS, the consultants suggested the following: 1) It is essential to develop and implement standardized educational programs for triage practitioners. 2) The number of beds needs to be adjusted and expanded. 3) It is necessary to secure the triage area and the yellow zone, and to provide essential equipment. 4) It must be considered to reinforce the manpower of triage practitioners, and to arrange a dedicated yellow zone medical staff.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".