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Record W4388714443 · doi:10.1055/s-0043-1776405

International Council for Standardization in Haematology Guidance for New Lot Verification of Coagulation Reagents, Calibrators, and Controls

2023· review· en· W4388714443 on OpenAlexaff
Robert C. Gosselin, Donna D. Castellone, Akbar Dorgalaleh, Kieron Hickey, Giuseppe Lippi, Karen A. Moffat, Rebecca O'Toole, Joseph Rigano

Bibliographic record

VenueSeminars in Thrombosis and Hemostasis · 2023
Typereview
Languageen
FieldMedicine
TopicClinical Laboratory Practices and Quality Control
Canadian institutionsMcMaster University
Fundersnot available
KeywordsStandardizationGuidelineMedicineMedical physicsHarmonizationCoagulation testingClinical PracticeMedical educationCoagulationComputer scienceFamily medicinePathologyInternal medicine

Abstract

fetched live from OpenAlex

The clinical laboratory uses commercial products with limited shelf life or certain expiry dates requiring frequent lot changes. Prior to implementation for clinical use, laboratories should determine the performance of the new reagent lot to ensure that there is no significant shift in reagent performance or reporting of patient data. This guideline has been written on behalf of the International Council for Standardization in Haematology (ICSH) to provide the framework and provisional guidance for clinical laboratories for evaluating and verifying the performance of new lot reagents used for coagulation testing. These ICSH Working Party consensus recommendations are based on good laboratory practice, regulatory recommendations, evidence emerged from scientific publications, and expert opinion and are meant to supplement regional standards, regulations, or requirements.

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)0.042
metaresearch head score (Gemma)0.046
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.042
Threshold uncertainty score0.222

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0420.046
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0100.008
Science and technology studies0.0010.003
Scholarly communication0.0040.003
Open science0.0050.003
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0110.009

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 head0.409
GPT teacher head0.474
Teacher spread0.065 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations8
Published2023
Admission routes1
Has abstractyes

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