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Record W1917188705 · doi:10.1111/cge.12077

A centralized approach to out‐of‐province genetic testing leads to cost savings: the Alberta experience

2012· article· en· W1917188705 on OpenAlexaffabout
Margaret Lilley, Susan Christian, Pamela Blumenschein, Stephanie Chan, Martin J. Somerville

Bibliographic record

VenueClinical Genetics · 2012
Typearticle
Languageen
FieldMedicine
TopicGenetic factors in colorectal cancer
Canadian institutionsAlberta HealthUniversity of AlbertaAlberta Health Services
Fundersnot available
KeywordsConsistency (knowledge bases)Genetic testingSelection (genetic algorithm)Computer scienceProcess (computing)Test (biology)Cost analysisTest strategyOperations managementResource (disambiguation)Operations researchReliability engineeringBusinessEngineeringBiologyEcologyGenetics

Abstract

fetched live from OpenAlex

The Genetic Resource Center (GRC) is a centralized process for requesting genetic testing that is not available within the province (Alberta, Canada). In order to assess potential cost savings associated with this process, all applications received by the GRC in 2010 were reviewed, and cost savings were recorded for statistical analysis. Seven areas of cost savings were identified: (i) negotiated pricing, (ii) laboratory selection, (iii) testing setup in-province, (iv) duplicate testing, (v) inappropriate testing, (vi) sequential testing and (vii) testing offered within the province.The total test cost of the 615 applications submitted in 2010 without the GRC process would have been $766,783 (Canadian dollars). A total cost savings of $112,201 was achieved through the GRC, which represents 15% of the total cost of requested testing ($112,201/$766,783). This is the first study to examine areas of cost savings for genetic testing sent out-of-province. The greatest cost savings resulted from the areas of laboratory selection and negotiated pricing. A centralized process to manage out-of-province genetic test requests results in consistency in testing and significant cost savings.

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)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation 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 score0.097
Threshold uncertainty score0.813

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.160
GPT teacher head0.405
Teacher spread0.245 · 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.

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

Citations7
Published2012
Admission routes2
Has abstractyes

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