[Triage clinic practice. Application of different systems of triage].
Bibliographic record
Abstract
INTRODUCTION: Emergency Services in Spain are suffering from a permanent increase in demand, making it difficult to fast and efficient they are required. The new situation has led to a transformation of the emergency departments with structural, organizational and functional changes, being triage system one of the key points. Triage systems are intended primarily to classify patients according to the level of urgency in order to prioritize the order of attendance, in addition to defining the most appropriate location, predict clinical outcomes and resource needs of patients, which contribute to the management of Emergency Service. DEVELOPMENT: Five models are currently recognized triage with a wide deployment among countries: Australian Triage Scale, Manchester Triage System, Emergency Severity Index, Canadian Triage and Acuity Scale, and Spanish Triage System, created from the Andorran Model of Triage (MAT). DISCUSSION: In Catalonia, we propose the implementation of a unique triage mode, MAT, and is considered a key intervention to improve the quality of care for emergencies, but from this deployment would be necessary to know if the results confirm this is a good system for managing all emergency services. CONCLUSION: Triage has become the hallmark of the hospital Emergency department who applies. It is an objective tool used to evaluate and improve the operation of the Emergency Services.
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.
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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.006 |
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".