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Record W4389286350 · doi:10.1097/ms9.0000000000001492

Vascular trauma injury evaluation in Khorramabad, Iran: a cross-sectional study

2023· article· en· W4389286350 on OpenAlexaff
Masoud Sharifian, Atefeh Marzban, Mania Beiranvand, Mohammad Javad Mahboubi, Mohammad Garshasebi

Bibliographic record

VenueAnnals of Medicine and Surgery · 2023
Typearticle
Languageen
FieldMedicine
TopicVascular Procedures and Complications
Canadian institutionsRunning Injury Clinic
Fundersnot available
KeywordsMedicineReferralMajor traumaShahidSurgeryComplicationRetrospective cohort studyPresentation (obstetrics)

Abstract

fetched live from OpenAlex

Background: Vascular trauma injuries are associated significantly with disabilities and mortality where prompt diagnosis and management are of great importance. Objectives: In this study, the authors aim to evaluate the pattern of vascular trauma injuries. Methods: This descriptive retrospective study was performed on patients with vascular injuries due to trauma referred to (Shohada Ashayer Hospital and Shahid Chamran Hospital, Khorramabad). Patients' files were evaluated for the following data: diagnostics, types of treatment and outcome, type of trauma, cause of trauma, anatomy of the injured site, duration of surgery, type of lesion, delayed complication, and requirement of revision surgery. The data obtained were descriptively evaluated using SPSSv22. Results: Of 233 patients studied, 95.3% were males. The mean age of the patients was 29.15±11.8 years. 82.8% of patients presented with penetrating trauma whereas 32.2% of patients had stab wound trauma. The most common sign at the time of referral was a loss of sensation in 54.9% of patients. Direct diagnosis based on clinical presentation was made in 79% of patients. The upper extremity was the most common site of vascular injury in 77.3% of patients with the involvement of radial and ulnar arteries, in 63.1%. 66.9% of patients underwent primary vascular repair, 92 received revision surgery, and 69 required blood transfusion. Conclusion: Epidemiological studies of vascular injury can help clinicians and local healthcare centres to understand the pattern of vascular trauma based on the geographical location and train trauma surgeons and medical staff to provide effective and timely management.

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)0.004
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 score0.025
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.239
GPT teacher head0.438
Teacher spread0.199 · 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
Published2023
Admission routes1
Has abstractyes

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