COOLING THE BURN WOUND AMONG THE CHILDREN AND TEENAGERS IN THE FIREFIGHTER PRACTICE
Bibliographic record
Abstract
Aim: The analysis of the cases of cooling the burns by NFRS firefighters. Material and methods: The data of Decision Support System of State Fire Brigade made accessible by the State Fire Bureau of the Operation Planning was analyzed concerning cooling the burns among the children between 1.01.2019-31.12.2020. 49 incidents were analyzed in terms of the mechanism, localiza¬tion, depth, extend of the burns, season of the year and day. Results: Burns were cooled in 1211 out of 126241 casualties, including 1023 of 7616 in fires and 188 of 118625 in local threats. Burn were cooled in 49 children out of 1211 casualties- 23 in local threats and 26 in fires. Cooling burns more often concerned in thermal (45), contact burns (27), I/II (48), up to 10% TBSA (32), in boys (25), 14-17 years (18), in October (9), from 1-11 p.m. (27) and in IV quarter of the year (19). Conclusions: 1. Among the injured the minor ones with the burns are not often cases. 2. Cooling the burns is more often associated with those ones injured in the fires and in boys. 3. Among the injured up to 17 years cooling the burns is more often seen during afternoon and autumn-winter season. 4. The fire¬fighters more often cool thermal, contact, superficial ones of minor burns and concerning different parts of the body within the upper its parts.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".