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Anaphylaxis: assessing patients with allergies

2008· review· en· W183320912 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEmergency Nurse · 2008
Typereview
Languageen
FieldMedicine
TopicFood Allergy and Anaphylaxis Research
Canadian institutionsSt. Peter's Hospital
Fundersnot available
KeywordsAnaphylaxisAllergyMedicineMedical emergencyImmunology

Abstract

fetched live from OpenAlex

Adam's assessment identified the presence of the most severe symptoms of anaphylaxis, respiratory difficulty and shock. The actions of the ambulance crew ensured that on arrival at the ED, Adam's condition was improving. However, it was found that despite the interventions already undertaken, Adam was suffering significant respiratory difficulty due to the cascade of events that occur in anaphylaxis. Adam was seen on arrival and the ALSG's (2005) assessment method proved to be the most effective to ensure that Adam was assessed and treated rapidly. The most beneficial aspect of this assignment is that the author can return to practice with this knowledge and will be more confident when faced with other patients with anaphylaxis. Understanding the underlying processes and symptoms has decreased the fear of 'not knowing' and will aid accurate assessment. It will also provide a good basis for educating parents of affected children and members of nursing teams.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.959
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
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 head0.041
GPT teacher head0.366
Teacher spread0.325 · 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