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Record W4392190722 · doi:10.1186/s12873-024-00951-w

Mortality of traumatic chest injury and its predictors across sub-saharan Africa: systematic review and meta-analysis, 2024

2024· review· en· W4392190722 on OpenAlexaboutno aff
Ousman Adal, Abiyu Abadi Tareke, Eyob Ketema Bogale, Tadele Fentabil Anagaw, Misganaw Guadie Tiruneh, Eneyew Talie Fenta, Destaw Endeshaw, Amare Mebrat Delie

Bibliographic record

VenueBMC Emergency Medicine · 2024
Typereview
Languageen
FieldMedicine
TopicTrauma Management and Diagnosis
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineFunnel plotMeta-analysisPublication biasChecklistSystematic reviewMortality rateObservational studyMEDLINEEmergency medicineSurgeryInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Globally, chest trauma remain as a prominent contributor to both morbidity and mortality. Notably, patients experiencing blunt chest trauma exhibit a higher mortality rate (11.65%) compared to those with penetrating chest trauma (5.63%). AIM: This systematic review and meta-analysis aimed to assess the mortality rate and its determinants in cases of traumatic chest injuries. METHODS: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist guided the data synthesis process. Multiple advanced search methods, encompassing databases such as PubMed, Africa Index Medicus, Scopus, Embase, Science Direct, HINARI, and Google Scholar, were employed. The elimination of duplicate studies occurred using EndNote version X9. Quality assessment utilized the Newcastle-Ottawa Scale, and data extraction adhered to the Joanna Briggs Institute (JBI) format. Evaluation of publication bias was conducted via Egger's regression test and funnel plot, with additional sensitivity analysis. All studies included in this meta-analysis were observational, ultimately addressing the query, what is the pooled mortality rate of traumatic chest injury and its predictors in sub-Saharan Africa? RESULTS: Among the 845 identified original articles, 21 published original studies were included in the pooled mortality analysis for patients with chest trauma. The determined mortality rate was nine (95% CI: 6.35-11.65). Predictors contributing to mortality included age over 50 (AOR 3.5; 95% CI: 1.19-10.35), a time interval of 2-6 h between injury and admission (AOR 3.9; 95% CI: 2.04-7.51), injuries associated with the head and neck (AOR 6.28; 95% CI: 3.00-13.15), spinal injuries (AOR 7.86; 95% CI: 3.02-19.51), comorbidities (AOR 5.24; 95% CI: 2.93-9.40), any associated injuries (AOR 7.9; 95% CI: 3.12-18.45), cardiac injuries (AOR 5.02; 95% CI: 2.62-9.68), the need for ICU care (AOR 13.7; 95% CI: 9.59-19.66), and an Injury Severity Score (AOR 3.5; 95% CI: 10.6-11.60). CONCLUSION: The aggregated mortality rate for traumatic chest injuries tends to be higher in sub-Saharan Africa. Factors such as age over 50 years, delayed admission (2-6 h), injuries associated with the head, neck, or spine, comorbidities, associated injuries, cardiac injuries, ICU admission, and increased Injury Severity Score were identified as positive predictors. Targeted intervention areas encompass the health sector, infrastructure, municipality, transportation zones, and the broader community.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.707
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0160.003
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.277
GPT teacher head0.443
Teacher spread0.166 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations13
Published2024
Admission routes1
Has abstractyes

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