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Record W1915459250 · doi:10.1309/ajcp80repiugvxph

Yearly Clinical Laboratory Test Expenditures for Different Medical Specialties in a Major Canadian City

2015· article· en· W1915459250 on OpenAlexafffundabout
Christopher Naugler, Roger E. Thomas, Tanvir Chowdhury Turin, Maggie Guo

Bibliographic record

VenueAmerican Journal of Clinical Pathology · 2015
Typearticle
Languageen
FieldMedicine
TopicClinical Laboratory Practices and Quality Control
Canadian institutionsCalgary Laboratory ServicesUniversity of Calgary
FundersUniversity of Calgary
KeywordsTest (biology)SpecialtyPer capitaMedicineFamily medicineMedical laboratoryRetrospective cohort studyCohortLaboratory testEnvironmental healthInternal medicineNursingPopulation

Abstract

fetched live from OpenAlex

OBJECTIVES: Very little is known about the relative contributions of physician specialty groups and individual physicians to overall clinical laboratory expenditures. The objectives of this study were to determine the costs of clinical laboratory test expenditures attributable to 30 medical specialties and the associated per capita physician expenditures for an entire major Canadian city. Only chemistry, hematology, and microbiology tests were included in this study. METHODS: Retrospective cohort study involving all physicians in Calgary, Canada, and surrounding areas (n = 3,499) and secondary data on laboratory test orders. RESULTS: Data were obtained on approximately 20 million test requests. The mean clinical laboratory test expenditure, in Canadian dollars, per physician was $27,945 for all physicians combined. Total expenditures by primary care physicians (family physicians and general practitioners) accounted for 58% of total expenditures. CONCLUSIONS: There was wide variation in clinical laboratory test expenditures among specialties and on a per capita basis within medical specialties.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.039
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.136
GPT teacher head0.479
Teacher spread0.343 · 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

Citations21
Published2015
Admission routes3
Has abstractyes

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