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Record W2488171002 · doi:10.1111/ijlh.12526

Standardization of haematology critical results management in adults: an International Council for Standardization in Haematology, ICSH, survey and recommendations

2016· article· en· W2488171002 on OpenAlexaff
Tee Beng Keng, Barbara De la Salle, G. Bourner, Anna Merino, J‐Y. Han, Y Kawai, Meng Peng, R. McCafferty

Bibliographic record

VenueInternational Journal of Laboratory Hematology · 2016
Typearticle
Languageen
FieldMedicine
TopicClinical Laboratory Practices and Quality Control
Canadian institutionsUniversity of Guelph
FundersSociedad Española de Hematología y Hemoterapia
KeywordsStandardizationProtocol (science)MedicineMedical laboratoryMedical educationFamily medicineMedical physicsPathologyAlternative medicineComputer science

Abstract

fetched live from OpenAlex

INTRODUCTION: These recommendations are intended to develop a consensus in the previously published papers as to which parameters and what values should be considered critical. A practical guide on the standardization of critical results management in haematology laboratories would be beneficial as part of good laboratory and clinical practice and for use by laboratory-accrediting agencies. METHODS: A working group with members from Europe, America, Australasia and Asia was formed by International Council for Standardization in Haematology. A pattern of practice survey of 21 questions was distributed in 2014, and the data were collected electronically by Survey Monkey. The mode, or most commonly occurring value, was selected as the threshold for the upper and lower alert limits for critical results reporting. RESULTS: A total of 666 laboratories submitted data to this study and, of these, 499 submitted complete responses. Full blood count critical results alert thresholds, morphology findings that trigger critical result notification, critical results alert list, notification process and maintenance of critical results management protocol are described. This international survey provided a snapshot of the current practice worldwide and has identified the existence of considerable heterogeneity of critical results management. CONCLUSION: The recommendations in this study represent a consensus of good laboratory practice. They are intended to encourage the implementation of a standardized critical results management protocol in the laboratory.

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)0.005
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.335
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.110
GPT teacher head0.431
Teacher spread0.321 · 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.

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

Citations19
Published2016
Admission routes1
Has abstractyes

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