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Record W2298701934 · doi:10.15273/dmj.vol42no1.6431

Costs and stewardship of laboratory tests in the Capital Health District

2015· article· en· W2298701934 on OpenAlexaffvenueabout
Robert G. Farmer

Bibliographic record

VenueDalhousie Medical Journal · 2015
Typearticle
Languageen
FieldMedicine
TopicClinical Laboratory Practices and Quality Control
Canadian institutionsDalhousie University
Fundersnot available
KeywordsNova scotiaStewardship (theology)Diagnostic testTest (biology)Health careBusinessResource useCapital equipmentResource (disambiguation)MedicineCapital (architecture)Medical emergencyOperations managementActuarial scienceEconomicsComputer scienceEmergency medicineEnvironmental resource managementPolitical scienceIndustrial organization

Abstract

fetched live from OpenAlex

Responsible use of diagnostic resources benefits the Canadian healthcare system. Educating clinicians about the costs of laboratory tests can reduce corresponding resource use, both by encouraging substitutions with appro- priate and less-expensive alternatives, and by reducing overall diagnostic workup loads where warranted. To that end, this paper presents the costs of commonly used laboratory tests, and of some less-expensive alternatives, for the Capital District Health Authority of Nova Scotia, Canada. It then compares the aggregate costs of representa- tive brief and elaborate workups for six common patient presentations. When used appropriately, initial workups composed of fewer test alternatives can save between $11.27 (abdominal pain) and $103.63 (shortness of breath) per workup without affecting patient care. Making judicious test substitutions can also provide savings. Clinicians are encouraged to consider these costs and alternatives when providing future patient care.

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)0.002
metaresearch head score (Gemma)0.016
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.766
Threshold uncertainty score0.471

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.057
GPT teacher head0.385
Teacher spread0.328 · 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

Citations2
Published2015
Admission routes3
Has abstractyes

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