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Record W2607884045 · doi:10.1111/ijlh.12678

Ten‐color 15‐antibody flow cytometry panel for immunophenotyping of lymphocyte population

2017· article· en· W2607884045 on OpenAlexaffabout
Amr Rajab, Olof Axler, Joseph C.K. Leung, M. Wozniak, Anna Porwit

Bibliographic record

VenueInternational Journal of Laboratory Hematology · 2017
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsImmunophenotypingPopulationMedicineLymphocytePathologyCD8ImmunologyFlow cytometryAntigen

Abstract

fetched live from OpenAlex

Summary We have developed a lymphoproliferative disorder screening tube ( LPD ‐ ST ) with the aim to provide comprehensive immunophenotyping of lymphocyte subsets with minimal need for additional testing. The LPD ‐ ST consists of CD4/kappa FITC , CD8/lambda PE , CD 3/ CD 14 ECD , CD 38 PC 5.5, CD 20/ CD 56 PC 7, CD 10 APC , CD 19 APC ‐A700, CD 5 APC ‐A750, CD 57/ CD 23 PB and CD 45 KO . The LPD ‐ ST was validated against previously used lymphocyte subset panels in Canada (n=60) and in Sweden (n=43) and against the OneFlow ™ LST (n=60). The LPD ‐ ST panel was then implemented in clinical practice using dried monoclonal antibody reagents (Duraclone ® ) on 649 patient samples in Sweden. In 204 of 649 samples (31%), a monotypic B‐cell population was found. Of these cases, a final diagnosis could be rendered in 106 cases (52%), and in the remainder, additional B‐cell immunophenotyping was performed. In 20 (3%) samples, an aberrant T‐cell population was confirmed by additional testing. Of 425 samples diagnosed as normal/reactive lymphoid tissue, 50 (12%) required additional immunophenotyping, mostly due to an abnormal CD 4/ CD 8 ratio. The LPD ‐ ST tube significantly minimizes the need for additional testing, improves the turn‐around time, and reduces the cost of LPD immunophenotyping. It is also suitable for investigating paucicellular samples such as cerebrospinal fluid or fine needle aspirates.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.429

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.027
GPT teacher head0.359
Teacher spread0.332 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations35
Published2017
Admission routes2
Has abstractyes

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