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Record W2413140130 · doi:10.69554/kczd5462

Using community triage centres or non-traditional care facilities during a flu pandemic or other infectious disease outbreak

2008· article· en· W2413140130 on OpenAlexaboutno aff
Eric A. Bone, Shawn Grono, David H. Johnson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsnot available
Fundersnot available
KeywordsOutbreakPandemicTriageInfectious disease (medical specialty)Coronavirus disease 2019 (COVID-19)Medical emergencyMedicineSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Disease2019-20 coronavirus outbreakVirologyEnvironmental healthPathology

Abstract

fetched live from OpenAlex

One assumption of pandemic planning is that, during an influenza outbreak, acute care facilities may be quickly overrun with patients and as such must prepare in advance. In order to operationalise one component of a pandemic plan, Capital Health in Edmonton, Alberta, piloted a mobile triage centre facility (portable isolation containment systems) and tested pandemic influenza triage and assessment guidelines in the winter of 2006-07. The mobile model provided emergency department surge capacity for communicable disease emergencies with scalable deployment capabilities. The deployable module has several advantages over a fixed structure like a community facility. The triage facility is a location for short-term treatments, such as intravenous therapy, prescriptions, medication distribution, and self-care education, which are needed during a pandemic influenza outbreak. Decanting infectious patients away from the emergency department protects a highly-vulnerable hospitalised group from viral transmission. Based on the pilot, it is found that community triage centres are a viable support option for emergency departments in an urban setting during pandemic influenza.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.149
Threshold uncertainty score0.999

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 studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.276
GPT teacher head0.416
Teacher spread0.140 · 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 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

Citations1
Published2008
Admission routes1
Has abstractyes

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