Peri-operative strategy in resuscitation of unstable injured surgical patients: a primer
Bibliographic record
Abstract
BACKGROUND: Trauma remains a leading cause of death, both for individuals under 40 in North America, and globally, where it contributes to ~10% of deaths annually. Thoughtful, timely, balanced resuscitation, especially in the peri-operative period for unstable injured surgical patients, is vital for optimizing outcomes. The advanced trauma life support protocol plays a pivotal role in early evaluation and management, emphasizing hemorrhage control and resuscitation strategies. OBJECTIVE: This narrative review provides a structured, evidence-based framework aimed at enhancing the educational experience of surgical trainees. It outlines key principles in peri-operative trauma resuscitation, emphasizing timely intervention, goal-directed fluid therapy, and damage control surgery (DCS) to improve patient outcomes. METHODS: A comprehensive Scale for Quality Assessment of Narrative Review Articles -guideline compliant literature search was conducted using PubMed and Google Scholar for English-language articles published between January 2000 and February 2024. The search included relevant medical subject headings terms. Additional studies were identified from reference lists. Extracted data were reviewed and organized using thematic analysis, focusing on historical perspectives, evidence-based practices, and the concept of DCS. RESULTS: Key findings from the 55 relevant studies selected underscore the importance of balanced fluid and blood product administration, the use of permissive hypotension in hemorrhagic shock, and the application of DCS principles. This review highlights educational strategies that foster a deeper understanding of trauma resuscitation practices, offering practical insights through case studies and technological innovations. CONCLUSION: This review serves as an educational resource for surgical trainees, equipping them with a robust understanding of evidence-based trauma resuscitation. By integrating historical context, modern practices, and emerging technologies, the review aims to enhance both the theoretical knowledge and practical skills necessary for managing unstable trauma patients. Emphasis is placed on interdisciplinary teamwork, continuous education, and personalized resuscitation strategies to improve clinical outcomes.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".