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Record W2156545903 · doi:10.3201/eid1801.101485

Use of Lean Response to Improve Pandemic Influenza Surge in Public Health Laboratories

2012· article· en· W2156545903 on OpenAlexafffundabout
Judith L. Isaac‐Renton, Yin Chang, Natalie Prystajecky, Martin Petric, Annie Mak, Brendan Abbott, Benjamin Paris, Kay Decker, Lauren Pittenger, Steven Guercio, Jeff Stott, Joseph D. Miller

Bibliographic record

VenueEmerging infectious diseases · 2012
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsPublic Health Agency of CanadaProvincial Health Services AuthorityUniversity of British Columbia
FundersUniversity of British ColumbiaProvincial Health Services Authority
KeywordsPandemicPublic healthMultidisciplinary approachInfluenza pandemicCoronavirus disease 2019 (COVID-19)Surge CapacityPandemic influenzaMedicineMedical emergencyEnvironmental healthBusinessPolitical scienceNursingInfectious disease (medical specialty)PathologyDisease

Abstract

fetched live from OpenAlex

A novel infl uenza A (H1N1) virus detected in April 2009 rapidly spread around the world.North American provincial and state laboratories have well-defi ned roles and responsibilities, including providing accurate, timely test results for patients and information for regional public health and other decision makers.We used the multidisciplinary response and rapid implementation of process changes based on Lean methods at the provincial public health laboratory in British Columbia, Canada, to improve laboratory surge capacity in the 2009 infl uenza pandemic.Observed and computer simulating evaluation results from rapid processes changes showed that use of Lean tools successfully expanded surge capacity, which enabled response to the 10-fold increase in testing demands. A novel infl uenza A (H1N1) virus was detected inMexico and the southwestern United States in early April 2009 (1).Within days after confi rmation that this virus was circulating in the western Canadian province of British Columbia, the number of requests for infl uenza diagnostic tests rapidly increased.It became evident that current operations would not enable the British Columbia Public Health Microbiology & Reference Laboratory (PHMRL), the major provider of infl uenza diagnosis for this province, to meet testing demands.We describe Lean processes that were implemented to rapidly expand surge capacity. Methods Prepandemic Testing for Infl uenzaThe PHMRL serves the entire health care system for the western Canadian province of British Columbia (population 4.45 million).Pandemic planning lead by the Canadian Public Health Laboratory Network included implementation of a reverse transcription PCR (RT-PCR) platform.Before the pandemic, sample data were entered into the Laboratory Information System (LIS) and barcoded in the Central Processing & Receiving section; the accessioned respiratory samples were then transported to the Virology Laboratory, located 3 fl oors away.In the Virology Laboratory, 1 laboratory assistant organized the samples and transferred aliquots into labeled tubes.Testing was conducted by 1 medical laboratory technologist; tasks included nucleic acid extraction, RT-PCR, analysis of results, and report of results into the LIS.One easyMag extractor (bioMérieux, Marcy l'Etoile, France) (capacity 22 patient samples) and 1 ABI 7900 RT-PCR machine (Applied Biosystems, Foster City, CA, USA) (capacity 92 patient samples) were used.These processes were conducted 10.5 h/d, 6 d/wk during the normal British Columbia infl uenza season (September-March) by 1 laboratory assistant and 2 medical laboratory technologists (1 technologist on each of 2 shifts).These assignments enabled PHMRL to meet prepandemic demand for infl uenza testing.Test results were available on the same day as arrival in PHMRL, except for weekends.Volumes seldom exceeded 50 samples/d.

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)0.007
metaresearch head score (Gemma)0.012
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.127
GPT teacher head0.407
Teacher spread0.280 · 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

Citations19
Published2012
Admission routes3
Has abstractyes

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