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Record W1975958931 · doi:10.1186/cc8514

Large-scale implementation of a critical care surge capacity management program

2010· article· en· W1975958931 on OpenAlexaffabout
B. A. Lawless, Julie Trpkovski

Bibliographic record

VenueCritical Care · 2010
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsSurge CapacityMedicineGeneral partnershipHealth careScale (ratio)Medical emergencyPandemicChristian ministryNursingCoronavirus disease 2019 (COVID-19)BusinessEconomic growthFinance

Abstract

fetched live from OpenAlex

In 2003 the outbreak of severe acute respiratory syndrome (SARS) in some jurisdictions around the world highlighted a number of areas in healthcare planning that could be improved for dealing with such disasters. In Canada, one of the noted system challenges was the significant impact that an increase in the number of critically ill patients had on access to care and to other hospital services. Many jurisdictions have undertaken large-scale pandemic planning; however, there is a paucity of tools available to help hospitals deal with the day to day challenges of surges in patient volumes. As part of a larger comprehensive Critical Care Strategy designed to improve access to care, improve the quality of care and improve health system integration, and in partnership with hospitals and healthcare workers, the Ontario Ministry of Health and Long-Term Care designed and implemented a critical care Surge Capacity Management Program.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.705
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 studies0.0000.000
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 head0.061
GPT teacher head0.493
Teacher spread0.433 · 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 designQualitative
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
Published2010
Admission routes2
Has abstractyes

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