Analysis of the change rules of first aid disease spectrums in Qingpu District
Bibliographic record
Abstract
Objective To understand the change rules of the disease spectrum of first-aid patients in the District of Qingpu, in order to improve the level of pre-hospital care. Methods The patients with pre-hospital emergency clinical data were analyzed retrospectively in 2012, 2013. Results In 2013, Pre-hospital emergency patients increased much than the last, And the proportion of women fronted the up-trend significantly; however, Men patients were more than women patients. And with 36-59 patients most each year. In 2013, patients aged from 60-64 years old, and more than 65 years old ratio was increased from 2012. The number of cases happened mostly in July, and the latter half of the year were higher than the first half, the number of patients increased obviously in the fourth quarter of 2013. A variety of trauma patients had the greatest number in each year. Conclusion First aid should be adjusted accordingly on the basis of the changes of disease spectrum, in order to improve the pre-hospital emergency rescue success rate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".