Consensus Recommendations of the Academic College of Emergency Experts in India on the Evaluation and Management of Polytrauma in Children Presenting to the Emergency Department in India
Bibliographic record
Abstract
India, with the highest rate of road traffic accident-related deaths among children in the world, urgently needs an integrated system of care and an evidence-based, consensus approach for the evaluation and management of critically injured children. To develop a country-specific guideline for the systematic assessment and emergency management of critically injured children, a consensus meeting comprising members of the Academic College of Emergency Experts was held at the National Institute of Health and Family Welfare, New Delhi, on November 7, 2023. This was followed by an exhaustive literature search on selected areas of concern and multiple online meetings to arrive at this consensus guideline. The process for developing developing India-specific guidelines was based on retrospective cohort studies and multicenter studies on the management of pediatric trauma in India. These studies were crucial for understanding the local epidemiology and management of pediatric trauma. The guideline and the clinical pathway have been produced as a tool for all healthcare workers, including the prehospital staff, the emergency department (ED) doctors, as well as nurses, surgeons, and intensive care unit physicians and nurses involved in the care of an injured child. The guideline includes the following key components: The current scenario of polytrauma in children in India; the trauma chain of survival; triage, and the systematic approach to a patient in the ED. This guideline aims to standardise the approach to injured children across the country. We urge the development and creation of a robust data repository of minimal standard data elements in all the EDs to facilitate systematic measurement of the care processes and patient outcomes, providing more evidence that can be used to further modify this guideline.
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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 | 0.053 | 0.124 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.011 | 0.008 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".