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Record W4387654409 · doi:10.52609/jmlph.v3i3.93

Snake Bites in The Arabian Peninsula: A Scoping Review

2023· review· en· W4387654409 on OpenAlexvenueno aff
Ibtihal Alsahabi, Ghadah Alenizi, Rawan Eskandarani

Bibliographic record

VenueThe Journal of Medicine Law & Public Health · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVenomous Animal Envenomation and Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAntivenomMedicineEnvenomationPeninsulaSnake bitesVIPeRGeographyFisheryVenom

Abstract

fetched live from OpenAlex

INTRODUCTIONThe aim of this study is to provide a comprehensive review of snake bites in the Arabian Peninsula. METHODS A scoping review was conducted from October to December 2022, and included sources from PubMed, Ovid, the Cochrane Database, reference lists of relevant articles, and grey literature sources such as ClinicalTrials.gov and the World Health Organization’s International Clinical Trials Registry Platform. The keywords used were “Arabian Peninsula”, “Saudi Arabia”, “Qatar”, “Kuwait”, “Oman”, “United Arab Emirates”, “Bahrain”, “Yemen”, “snake venom”, “snake bite”, and “envenomation”. The inclusion criteria for selecting studies were those that explored snake bites in various regions of the Arabian Peninsula. RESULTS 28 studies were included, with a total of 16,602 snake bite cases. In 78.57% of cases, the initial presentation was a local injury. Haematological manifestations were seen in several of the reported cases, while some cases showed neurological symptoms and cardiac manifestation. Leucocytosis, thrombocytopenia/thrombocytosis, and acute kidney injury and proteinuria were also observed. The administered dose of antivenom varied, and post-antivenom complications were seen in less than one third of the reported cases. CONCLUSIONThe current body of literature does not provide a concise management plan for snake bite in the Arabian Peninsula. We provide a proposed plan for treating and monitoring such 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)0.012
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.745
Threshold uncertainty score0.446

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.222
GPT teacher head0.453
Teacher spread0.231 · 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 designNot applicable
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

Citations0
Published2023
Admission routes1
Has abstractyes

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