Emergency Data Management - Overcoming (Information) Borders.
Bibliographic record
Abstract
BACKGROUND: In order to improve access to critical patient data in case of emergency, many countries have begun or intend to implement emergency datasets. In Germany, the German Medical Association developed a medical emergency dataset (MED), which provides the possibility to store information on prior diagnoses, medications, allergies and other emergency-relevant information on the German Electronic Health Card. OBJECTIVES: The aim of the study is to evaluate how the MED can be used internationally. METHODS: A total of 64 paper-based emergency data sets were completed by primary care physicians in Germany, and were then evaluated by German clinicians, emergency physicians, and paramedics on the basis of fictitious emergency scenarios. Thirty randomly selected MEDs were then translated into English and will be evaluated by international emergency physicians and paramedics. RESULTS: In Germany, clinicians, emergency physicians and paramedics rated the emergency data set as very useful or useful in more than 70% of the reviewed cases. The international evaluation will start in September 2016, so these results are pending at this time. CONCLUSION: The first study results from Germany indicate high potential benefits of the emergency data set in real patient care situations. The subsequent tests will show whether the MED is also suitable for international use.
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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 | 0.017 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.012 |
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".