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Record W2100785440 · doi:10.1017/s148180350000806x

Canadian emergency department preparedness for a nuclear, biological or chemical event

2003· article· en· W2100785440 on OpenAlexaffabout
Daniel Kollek

Bibliographic record

VenueCanadian Journal of Emergency Medicine · 2003
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsMcMaster UniversityHamilton Health SciencesHealth Sciences Centre
Fundersnot available
KeywordsPreparednessTerrorismMedical emergencyMedicineMass CasualtyEvent (particle physics)AccidentalChemical warfareChemical agentsBiological warfareEnvironmental healthToxicologyPolitical scienceEngineeringLawBiology

Abstract

fetched live from OpenAlex

Since the terror attacks of September 11th, emergency departments across North America have become more aware of the need to be prepared to deal with a mass casualty terror event, particularly one involving nuclear, biological or chemical contaminants. The effects of such an attack could also be mimicked by accidental release of toxic chemicals, radioactive substances or biological agents unrelated to terrorist activity. The purpose of this study was to review the risks and characteristics of these events and to assess the preparedness of Canadian emergency departments to respond. This was done by means of a survey, which showed a significant risk of a mass casualty event (most likely chemical) coupled with a deficiency in preparedness -- most notably in the availability of appropriate equipment, antidotal therapy and decontamination capability. There were also significant deficiencies in the ability to respond to a major biologic or nuclear event.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.125
Threshold uncertainty score0.252

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.001

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 head0.196
GPT teacher head0.448
Teacher spread0.252 · 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 source (direct Gemma or distilled Codex), 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

Citations23
Published2003
Admission routes2
Has abstractyes

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