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Record W4379113663 · doi:10.1515/cclm-2023-0502

Point-of-care testing performed by healthcare professionals outside the hospital setting: consensus based recommendations from the IFCC Committee on Point-of-Care Testing (IFCC C-POCT)

2023· article· en· W4379113663 on OpenAlexaff
Adil I. Khan, Busadee Pratumvinit, Ellis Jacobs, Gerald J. Kost, Heba Kary, Jan Balla, Julie Shaw, Lara Milevoj Kopčinović, Michel Vaubourdolle, Paloma Oliver, Paul Jarvis, Prasad V. A. Pamidi, Rajiv T. Erasmus, Ruth O’Kelly, Samarina Musaad, Sverre Sandberg

Bibliographic record

VenueClinical Chemistry and Laboratory Medicine (CCLM) · 2023
Typearticle
Languageen
FieldMedicine
TopicClinical Laboratory Practices and Quality Control
Canadian institutionsCanadian Electricity AssociationUniversity of Ottawa
Fundersnot available
KeywordsPoint-of-care testingMedicineMedical laboratoryPoint of careHealth careMedical emergencyIntensive care medicineMedical physicsPathology

Abstract

fetched live from OpenAlex

The International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Committee on Point-of-Care Testing (C-POCT) supports the use of point-of-care testing (POCT) outside of the hospital setting performed by healthcare professionals without formal laboratory education because of its numerous benefits. However, these benefits are associated with risks that must be managed, to ensure the provision of reliable test results and minimize harm to the patient. Healthcare professionals, local regulatory bodies, accredited laboratories as well as manufacturers should actively be engaged in education, oversight and advice to ensure that the healthcare professional selects the appropriate equipment and is able to analyze, troubleshoot and correctly interpret the point-of-care (POC) test results.

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)0.191
metaresearch head score (Gemma)0.243
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.191
Threshold uncertainty score0.998

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1910.243
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.006
Bibliometrics0.0090.006
Science and technology studies0.0040.005
Scholarly communication0.0080.004
Open science0.0120.008
Research integrity0.0160.018
Insufficient payload (model declined to judge)0.0020.004

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 head0.083
GPT teacher head0.417
Teacher spread0.334 · 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
Domainnot available
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

Citations30
Published2023
Admission routes1
Has abstractyes

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