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Record W4375928635 · doi:10.36740/emems202301105

COOLING THE BURN WOUND AMONG THE CHILDREN AND TEENAGERS IN THE FIREFIGHTER PRACTICE

2023· article· en· W4375928635 on OpenAlexaboutno aff
Leszek Marzec, Łukasz Czyżewski, Łukasz Dudziński

Bibliographic record

VenueEmergency Medical Service · 2023
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineQuarter (Canadian coin)Fire brigadeMedical emergencyEmergency medicineEngineeringAeronauticsGeography

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.047
GPT teacher head0.434
Teacher spread0.387 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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