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Record W2281891893 · doi:10.1128/cvi.00001-16

Flow Cytometry, a Versatile Tool for Diagnosis and Monitoring of Primary Immunodeficiencies

2016· review· en· W2281891893 on OpenAlex

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

VenueClinical and Vaccine Immunology · 2016
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmunodeficiency and Autoimmune Disorders
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsFlow cytometryImmune systemImmunologyComputational biologyBiologyDiseaseMedicineBioinformaticsPathology

Abstract

fetched live from OpenAlex

Genetic defects of the immune system are referred to as primary immunodeficiencies (PIDs). These immunodeficiencies are clinically and immunologically heterogeneous and, therefore, pose a challenge not only for the clinician but also for the diagnostic immunologist. There are several methodological tools available for evaluation and monitoring of patients with PIDs, and of these tools, flow cytometry has gained prominence, both for phenotyping and functional assays. Flow cytometry allows real-time analysis of cellular composition, cell signaling, and other relevant immunological pathways, providing an accessible tool for rapid diagnostic and prognostic assessment. This minireview provides an overview of the use of flow cytometry in disease-specific diagnosis of PIDs, in addition to other broader applications, which include immune phenotyping and cellular functional measurements.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.994
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.060
GPT teacher head0.354
Teacher spread0.294 · 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