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Record W1969792180 · doi:10.5505/tjtes.2011.60938

Analysis of trauma patients in a rural hospital in Turkey

2011· article· en· W1969792180 on OpenAlexaff
Nurettin Kahramansoy, Hayri Erkol, Feyzi Kurt, Necla Gürbüz, Murat Bozgeyik, Aysu Kıyan

Bibliographic record

VenueUlusal travma dergisi · 2011
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsPediatric Oncology Group
Fundersnot available
KeywordsMedicineEmergency departmentEpidemiologyInjury preventionPoison controlEmergency medicineOccupational safety and healthHead and neckMortality rateSurgeryPediatricsInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: There is a grey zone about the epidemiology of trauma in eastern Turkey. The present study was aimed at obtaining data on this subject. METHODS: Trauma patients who applied to the emergency department (ED) between January 2006 and December 2007 were analyzed. RESULTS: There were 6183 patients, of whom 87% were male. The mean age was 26.2 ± 13.6 years. Assault was the most common cause (63.2%). Motor vehicle injury (MVI) and fall were encountered at frequencies of 21.2% and 6.5%, respectively. The most frequently injured body regions were head-neck and extremities. The majority of patients were managed and discharged from the ED (89.8%) with no consultation (81.8%). Interestingly, the discharge rate of assault cases was 98.7%. Patients were hospitalized (4.2%) mostly for MVI (32.6%) and fall (19%); however, hospitalization rates for firearm and piercing/cutting injury (36.1% and 16.7%) were significantly high. Among the transported patients (5.3%), the rates of MVI and fall were high (41.5% and 24.3%, respectively). In groups, for burn and firearm injuries, these were 42.1% and 24.1%, respectively. Forty-eight patients (0.8%) died, mostly from MVI by number, but by self-infliction and firearm by rate (8.3% and 6%). CONCLUSION: Assault cases caused an excessive trauma patient density in the ED, as 98.7% were discharged from the ED. Further studies are needed regarding the high rate of assault cases.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.756

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.259
Teacher spread0.239 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations11
Published2011
Admission routes1
Has abstractyes

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