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Record W4390662513 · doi:10.1111/ijlh.14212

ICSH guidance for internal quality control policy for blood cell counters

2024· article· en· W4390662513 on OpenAlexafffund
R. McCafferty, George S. Cembrowski, Barbara De la Salle, Mingting Peng, Eloísa Urrechaga

Bibliographic record

VenueInternational Journal of Laboratory Hematology · 2024
Typearticle
Languageen
FieldMedicine
TopicClinical Laboratory Practices and Quality Control
Canadian institutionsUniversity of Alberta
FundersUniversiti MalayaUniversity of Alberta
KeywordsDiversity (politics)Control (management)Quality (philosophy)Computer sciencePolitical science

Abstract

fetched live from OpenAlex

This paper is a description of the ICSH guidance for internal quality control (IQC) policy for blood cell counters. It follows from and links to a separate ICSH review for such policies and practices. The ICSH has gathered information regarding the current state of practice through review of published guidance from regulatory bodies, a questionnaire to six major cell counter manufacturers and a survey issued to 191 diagnostic laboratories in four countries (China, the Republic of Ireland, Spain, and the United Kingdom) on their IQC practice and approach to the use of commercial IQC materials. This has revealed diversity both in guidance and in practice around the world. There is diversity in guidance from regulatory organizations in regard to IQC methods each recommends, clinical levels to use and frequency to run commercial controls, and finally recommended sources of commercial control materials. The diversity in practice among clinical laboratories spans the areas of IQC methods used, derivation of target values, and action limits used with commercial control materials, and frequency of running commercial controls materials. These findings and their implications for IQC Practice are addressed in this guidance document, which proposes a harmonized approach to address the issues faced by diagnostic laboratories.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.850
Threshold uncertainty score0.685

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
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 science0.0000.000
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 head0.033
GPT teacher head0.441
Teacher spread0.408 · 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 designNot applicable
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

Citations5
Published2024
Admission routes2
Has abstractyes

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