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Record W1486923278 · doi:10.1002/0471142956.cy0637s72

High‐Sensitivity Detection of PNH Red Blood Cells, Red Cell Precursors, and White Blood Cells

2015· article· en· W1486923278 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

VenueCurrent Protocols in Cytometry · 2015
Typearticle
Languageen
FieldImmunology and Microbiology
TopicComplement system in diseases
Canadian institutionsLondon Health Sciences CentreToronto General HospitalUniversity Health Network
Fundersnot available
KeywordsParoxysmal nocturnal hemoglobinuriaFlow cytometryImmunologyMonocyteMedicineCD59Antibody

Abstract

fetched live from OpenAlex

Flow cytometry is the method of choice to 'diagnose' paroxysmal nocturnal hemoglobinuria (PNH) and has led to improved patient management. Most laboratories have limited experience with PNH testing, and many different flow approaches are used. Careful selection and validation of antibody conjugates has allowed the development of reagent cocktails suitable for detection of PNH RBCs, CD71+ reticulocytes, and WBCs in clinical/sub-clinical PNH samples. A CD235a-FITC/CD59-PE assay was developed capable of detecting Type III PNH RBCs at 0.01% sensitivity. A protocol targeting immature CD71+ RBCs can detect PNH reticulocytes at similar sensitivity. Four-color FLAER-based neutrophil and monocyte assays were developed to detect PNH phenotypes at a level of 0.01% and 0.04% sensitivity, respectively. For instrumentation with five or more PMTs, a single-tube 5-color FLAER/CD157-based assay to simultaneously detect PNH neutrophils and monocytes is described. Using these standardized approaches, results have demonstrated good intra- and inter-laboratory performance characteristics even in laboratories with little prior experience performing PNH testing.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.035
GPT teacher head0.298
Teacher spread0.264 · 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