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Best practice guidelines on reference interval harmonization in Canada: Evidence-based recommendations from the CSCC working group on reference interval harmonization (CSCC WG-hRI)

2025· article· en· W4412978962 on OpenAlexafffundabout
Mary Kathryn Bohn, Cynthia Balion, George S. Cembrowski, Christine Collier, Vincent De Guire, Victoria Higgins, Benjamin Jung, Olivia Landon, Zahraa Mohammed‐Ali, David Seccombe, Albert K.Y. Tsui, Allison A. Venner, Nicole White Al-Habeeb, Khosrow Adeli

Bibliographic record

VenueClinical Biochemistry · 2025
Typearticle
Languageen
FieldMedicine
TopicPhonocardiography and Auscultation Techniques
Canadian institutionsSickKids FoundationCalgary Laboratory ServicesUniversity of CalgaryMount Sinai HospitalHôpital Maisonneuve-RosemontUniversity of AlbertaUniversity Health NetworkTellabs (Canada)Brampton Civic HospitalThe Scarborough HospitalUniversity of British ColumbiaHospital for Sick ChildrenMcMaster UniversityUniversity of Toronto
FundersAlberta Precision LaboratoriesCanadian Society of Clinical Chemists
KeywordsHarmonizationInterval (graph theory)Group (periodic table)MedicineMathematicsChemistry

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.106
metaresearch head score (Gemma)0.245
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.964
Threshold uncertainty score0.635

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1060.245
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0080.013
Bibliometrics0.0180.021
Science and technology studies0.0050.005
Scholarly communication0.0130.004
Open science0.0210.006
Research integrity0.0120.014
Insufficient payload (model declined to judge)0.0080.003

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 head0.211
GPT teacher head0.434
Teacher spread0.223 · 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.

Study designNot applicable
DomainMethods
GenreMethods

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

Citations1
Published2025
Admission routes3
Has abstractno

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