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Record W2950014010 · doi:10.1093/labmed/lmz016

Cutoff Value for Correcting White Blood Cell Count for Nucleated Red Blood Cells: What is it? Why is it Important?

2019· article· en· W2950014010 on OpenAlex
Benie T. Constantino, Gilbert Keith Q Rivera

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

VenueLaboratory Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsSunnybrook Health Science Centre
Fundersnot available
KeywordsCutoffHematology analyzerNucleated Red Blood CellPeripheral bloodWhite blood cellHematologyRed blood cellBlood cellMedicineComplete blood countImmunologyInternal medicineAndrologyBiologyPregnancyPhysicsGeneticsFetus

Abstract

fetched live from OpenAlex

Nucleated red blood cells (RBCs) are normally observed in the peripheral blood of neonates and during pregnancy. Under other conditions, the presence of nucleated RBCs in circulating blood indicates disorder in the blood-producing mechanism. The increased presence of nucleated RBCs, however, falsely elevates the leukocyte count, as measured by most automated hematology analyzers, warranting a manual correction of the leukocyte count. For a long time, cutoff values for correcting white blood cell (WBC) count for the presence of nucleated RBCs have been used regularly, particularly in developing countries. However, because those values are largely subjective, they can vary widely between laboratories worldwide. These varied cutoff values include 1, 5, 10, 20, and 50; it appears that the numbers 5 and 10 are the most common values used in corrections; the reasons require further elucidation. In this article, we discuss the merits of correcting the WBC count for nucleated RBCs at certain cutoff points.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.254
Teacher spread0.243 · 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