Tele-Pediatric Intensive Care for Critically Ill Children in Syria
Bibliographic record
Abstract
BACKGROUND: Armed conflicts can result in humanitarian crises and have major impacts on civilians, of whom children represent a significant proportion. Usual pediatric medical care is often disrupted and trauma resulting from war-related injuries is often devastating. High pediatric mortality rates are thus experienced in these ravaged medical environments. INTRODUCTION: Using simple communication technology to provide real-time management recommendations from highly trained pediatric personnel can provide substantive clinical support and have a significant impact on pediatric morbidity and mortality. MATERIALS AND METHODS: We implemented a "Tele-Pediatric Intensive Care" program (Tele-PICU) to provide real-time management consultation for critically ill and injured pediatric patients in Syria with intensive care needs. RESULTS: Over the course of 7 months, 19 cases were evaluated, ranging in age from 1 day to 11 years. Consultation questions addressed a wide range of critical care needs. Five patients are known to have survived, three were transferred, five died, and six outcomes were unknown. DISCUSSION: Based on this limited undertaking with its positive impact on survival, further development of Tele-PICU-based efforts with attention to implementation and barriers identified through this program is desirable. CONCLUSION: Even limited Tele-PICU can provide timely and potentially lifesaving assistance to pediatric care providers. Future efforts are encouraged.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".