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Record W2138313805 · doi:10.12968/jpar.2012.4.2.84

Carbon monoxide poisoning: a comprehensive review for prehospital specialists

2012· review· en· W2138313805 on OpenAlexaff
Caroline Whitson, Rodrick Lim, Naveen Poonai

Bibliographic record

VenueJournal of Paramedic Practice · 2012
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeme Oxygenase-1 and Carbon Monoxide
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineCarbon monoxide poisoningRhabdomyolysisIntensive care medicineCarboxyhemoglobinEmergency Care PractitionerEmergency medicineVomitingMajor traumaAnesthesiologyIncidence (geometry)CO poisoningEpidemiologyCombat Medical TechnicianMedical emergencyAnesthesiaPoison controlPediatricsCarbon monoxideInternal medicineContinuing professional development

Abstract

fetched live from OpenAlex

As one of the leading causes of poisonings worldwide, it is imperative that prehospital specialists are aware of carbon monoxide (CO) poisoning and its management. Awareness of the epidemiology, and the common presentations of CO poisoning may lead to prompt evaluation and early initiation of life-saving therapy. Children under 5 years of age have the highest incidence of CO-related ER visits and are at greatest risk of CO toxicity. The clinical features are nonspecific and misdiagnoses are common. Therefore, prehospital providers should have a high index of suspicion for CO intoxication in patients that experience headache, vomiting, or altered level of consciousness following exposure to hydrocarbon combustion within an enclosed space. A carboxyhaemoglobin level is a quick and reliable way to diagnose CO exposure. To prevent complications such as altered cerebellar function, seizures, rhabdomyolysis and dysrhythmias, early recognition and treatment is imperative. Removal from the source of exposure and the provision of 100% oxygen form the cornerstone of management. Preventive strategies should also be explored in susceptible populations.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.920
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.065
GPT teacher head0.400
Teacher spread0.335 · 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 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
Published2012
Admission routes1
Has abstractyes

Explore more

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