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Record W3092124900 · doi:10.1515/cclm-2020-1414

IFCC Interim Guidelines on Biochemical/Hematological Monitoring of COVID-19 Patients

2020· article· en· W3092124900 on OpenAlexaff
Simon Thompson, Mary Kathryn Bohn, Nicasio Mancini, Tze Ping Loh, Cheng-Bin Wang, Matthias Grimmler, Kwok-Yung Yuen, Robert Mueller, David D. Koch, Sunil Sethi, William D. Rawlinson, Massimo Clementi, Rajiv T. Erasmus, Marc Leportier, Gye Cheol Kwon, María Elizabeth Menezes, Maria‐Magdalena Patru, Maurizio Gramegna, Krishna Singh, Osama Najjar, Maurizio Ferrari, Giuseppe Lippi, Khosrow Adeli, Andrea R. Horvath

Bibliographic record

VenueClinical Chemistry and Laboratory Medicine (CCLM) · 2020
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineInterimIntensive care medicineCoronavirus disease 2019 (COVID-19)Medical laboratoryPandemicRisk stratificationClinical trialInfectious disease (medical specialty)DiseasePathologyInternal medicine

Abstract

fetched live from OpenAlex

Routine biochemical and hematological tests have been reported to be useful in the stratification and prognostication of pediatric and adult patients with diagnosed coronavirus disease (COVID-19), correlating with poor outcomes such as the need for mechanical ventilation or intensive care, progression to multisystem organ failure, and/or death. While these tests are already well established in most clinical laboratories, there is still debate regarding their clinical value in the management of COVID-19, particularly in pediatrics, as well as the value of composite clinical risk scores in COVID-19 prognostication. This document by the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Task Force on COVID-19 provides interim guidance on: (A) clinical indications for testing, (B) recommendations for test selection and interpretation, (C) considerations in test interpretation, and (D) current limitations of biochemical/hematological monitoring of COVID-19 patients. These evidence-based recommendations will provide practical guidance to clinical laboratories worldwide, underscoring the contribution of biochemical and hematological testing to our collective pandemic response.

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)0.001
metaresearch head score (Gemma)0.344
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.645
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.344
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.242
GPT teacher head0.520
Teacher spread0.278 · 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

Citations52
Published2020
Admission routes1
Has abstractyes

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