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Record W7096009128

Airline and EpiPen ® Marketer Want Travelers at Risk of Severe Allergic Reactions to Feel Better Prepared

2016· article· en· W7096009128 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicTopic Modeling
Canadian institutionsnot available
Fundersnot available
KeywordsAnaphylactic reactionsAnaphylaxisAllergic reactionRisk management
DOInot available

Abstract

fetched live from OpenAlex

they are working together on a new initiative that will see the Calgary-based airline equip all of its aircraft with EpiPen ® and EpiPen ® Jr (0.3 mg and 0.15 mg epinephrine) Auto-Injectors. WestJet plans to add the newly redesigned EpiPen ® and EpiPen ® Jr Auto-Injectors, marketed by King Pharmaceuticals Canada™, to the first aid kits in each of its aircraft. Epinephrine is the drug of choice for the emergency treatment of severe allergic reactions (also known as anaphylaxisa). EpiPen ® and EpiPen ® Jr Auto-Injectors are the country’s most-dispensedb epinephrine auto-injectors. WestJet spokesperson Tyson Matheson, VP People Relations and Culture, said the process of equipping the entire WestJet fleet with EpiPen ® Auto-Injectors is expected to be completed by early 2011. According to Matheson, the airline’s aircraft currently carry syringes and vials of epinephrine. For WestJet guests at risk of severe, potentially life-threatening allergic reactions, the redesigned EpiPen ® Auto-Injector offers the enhanced safety feature of needle protection before and after use. “The new EpiPen ® is designed for simplicity in an anaphylactic crisis, and we are pleased to have it available on all WestJet flights, ” said Matheson. “We obviously take the welfare and health of all WestJet guests very seriously and believe that this initiative will offer peace of mind for our guests at risk of anaphylaxis.” Travelers at risk of severe allergic reactions are ultimately responsible for taking all the necessary

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.844
Threshold uncertainty score0.249

Codex and Gemma teacher scores by category

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

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 head0.014
GPT teacher head0.223
Teacher spread0.209 · 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 designOther design
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

Citations0
Published2016
Admission routes1
Has abstractyes

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