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Record W4410323597 · doi:10.14744/ortst.79693

Clinical Outcome of Lower Extremity Firearm Injuries in Adult Civilians: Shotgun versus Pistol Wounds

2025· article· en· W4410323597 on OpenAlexaboutno aff
Serkan Aydın

Bibliographic record

VenueOrthopedic Surgery and Trauma Journal · 2025
Typearticle
Languageen
FieldMedicine
TopicTraumatic Ocular and Foreign Body Injuries
Canadian institutionsnot available
Fundersnot available
KeywordsShotgunMedicineSurgeryMedical emergencyBiology

Abstract

fetched live from OpenAlex

Background and Aims:The objective of this study was to compare the effects of firearm injury types on disability, length of hospital stay, and functional and clinical outcomes in patients with shotgun or pistol wounds.Factors affecting morbidity in firearm injuries were also investigated. Materials and Methods:In this multicenter study, 124 patients with at least two years of follow-up over a 10-year period were retrospectively analyzed.Patients were categorized into two groups-shotgun injuries and pistol injuries-based on the type of weapon that caused their wounds.The Lower Extremity Functional Scale (LEFS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and Short Form-36 Health Survey (SF-36) were used to assess outcomes.Results: The mean age of patients with firearm injuries was 36.48 11.25 years, with a followup period of 42.5416.7 months.Comparisons between the groups revealed no statistically significant differences in age, Injury Severity Score (ISS), WOMAC, SF-36, and LEFS scores (p>0.05).However, the length of hospital stay and follow-up duration were significantly longer in patients with shotgun injuries compared to those with pistol injuries (p<0.05). Conclusion:According to the main results of this study, clinical scores were found to be worse and length of stay was longer in shotgun wounds compared to pistol wounds.Neurovascular injury, soft tissue complications, and high injury severity may also negatively affect clinical outcomes.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.128
Threshold uncertainty score0.774

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.043
GPT teacher head0.351
Teacher spread0.307 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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