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Record W3110678891 · doi:10.1093/jalm/jfaa200

Point-of-Care Testing: The Good, the Bad, and the Laboratory Oversight

2020· article· en· W3110678891 on OpenAlex
Julie Shaw

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Journal of Applied Laboratory Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicClinical Laboratory Practices and Quality Control
Canadian institutionsOttawa HospitalCanadian Electricity AssociationUniversity of Ottawa
Fundersnot available
KeywordsTurnaround timePoint-of-care testingTest (biology)Point of careMedicinePatient careLaboratory testMedical physicsMedical emergencyMedical laboratoryOperations managementEngineeringPathologyNursingBiology

Abstract

fetched live from OpenAlex

Point-of-care testing (POCT) is a rapidly expanding area of laboratory medicine. POCT is near-patient testing that is performed near or at the site of a patient, with the result leading to possible change in the care of the patient (1). There are many advantages to POCT, but there are also many challenges, which can jeopardize the quality of the test results. POCT unquestionably has faster turnaround time for results compared with testing sent to a central laboratory. No transport time is required—transport time adds to turnaround time regardless of whether the central laboratory is on-site or the specimens are sent to an outside laboratory. The person ordering the test can be the same person who performs the test, eliminating delays making orders and collecting specimens. The volume of specimen required for POCT is generally much smaller than that required for traditional laboratory tests, and less volume...

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.

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)0.008
metaresearch head score (Gemma)0.006
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: none
Teacher disagreement score0.616
Threshold uncertainty score0.752

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.041
GPT teacher head0.308
Teacher spread0.267 · 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