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Record W4229453645 · doi:10.4314/rjmhs.v5i1.5

Prevalence of Surgical site Infection among Adult Patients at a Rural District Hospital in Southern Province, Rwanda

2022· article· en· W4229453645 on OpenAlexaff
Deborah Mukamuhirwa, Omondi Lilian, Vedaste Baziga, Cecile Ingabire, Christian Ntakirutimana, Joselyne Mukantwari, Emerthe Nyirasafari, Vedaste Bagweneza, Innocent Ngerageze, Marie Christine Umutesi

Bibliographic record

VenueRwanda Journal of Medicine and Health Sciences · 2022
Typearticle
Languageen
FieldMedicine
TopicSurgical site infection prevention
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineSurgical site infectionCaesarean sectionHuman immunodeficiency virus (HIV)District hospitalGeneral surgerySurgeryFamily medicinePregnancy

Abstract

fetched live from OpenAlex

Background: Globally, postoperative surgical site infection (SSI) is among the top causes of morbidity and mortality in patients undergoing surgery. Objectives: This study aimed to determine the prevalence of SSI among adult patients that underwent surgery at a hospital in the Southern Province, Rwanda. Method: The study design was cross-sectional and used structured questionnaires, interviews and reviewed patients' file records. Data were collected on 122 participants selected using the convenient sampling strategy. Statistical Package for Social Sciences version 2020 was used to analyze the data. Results: Most (86.1%) of the participants were females, the majority (48.4%) were aged 28-37 years. The prevalence of SSI was 8.2%, and most (90%) of the infected patients had undergone Caesarean section. Being HIV positive increased the risk for developing SSI. (X2: 9.604, df:1, CI: 1.7053; 19.8652; p value=0.014). Conclusion: The prevalence of SSI was 8.2%. Therefore, there is a need for enhancing preventive measures, early detection and treatment that will reduce the comorbidities of infected patients. HIV patients would need further attention.

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)0.000
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 score0.011
Threshold uncertainty score0.754

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.304
Teacher spread0.290 · 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

Citations14
Published2022
Admission routes1
Has abstractyes

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