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Record W2117809947 · doi:10.5555/2433508.2433798

Use of simulation in support of analysis and improvement of blood collection process

2010· article· en· W2117809947 on OpenAlexaffabout
Benjamin de Mendonca, Andrew Phibbs, Joshua Vandermeer, Zbigniew J. Pasek

Bibliographic record

VenueWinter Simulation Conference · 2010
Typearticle
Languageen
FieldMedicine
TopicClinical Laboratory Practices and Quality Control
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsData collectionBlood collectionProcess (computing)Computer scienceAnalytic hierarchy processOrder (exchange)Operations researchRisk analysis (engineering)EngineeringMedicineMedical emergency

Abstract

fetched live from OpenAlex

This paper deals with efforts aiming to improve processes associated with the blood specimen order and collection process in one of the Canada's largest and most diverse health care facilities. The analytical specimen testing is defined to have five sub-systems that synergize to execute the order, collection, transportation, analysis and result reporting of blood-based analytic laboratory tests. In the project current processes were defined in accordance to the standard operating procedures and other hospitals techniques and benchmarked to other known blood collection and analysis systems. The recommendations and implementation strategy to address the challenges associated with the errors and delays in the blood collection system in the emergency department at Sunnybrook were developed and prioritized using an analytical hierarchy process.

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)0.004
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.076
GPT teacher head0.403
Teacher spread0.326 · 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 designSimulation or modeling
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

Citations3
Published2010
Admission routes2
Has abstractyes

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