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Record W2119984494 · doi:10.1309/lmz1gky9lqtvfbl7

Red Cell Distribution Width, Revisited

2013· article· en· W2119984494 on OpenAlexaff
Benie T. Constantino

Bibliographic record

VenueLaboratory Medicine · 2013
Typearticle
Languageen
FieldMedicine
TopicInflammatory Biomarkers in Disease Prognosis
Canadian institutionsCARE Canada
Fundersnot available
KeywordsRed blood cell distribution widthAnisocytosisCoefficient of variationMean corpuscular volumeHistogramStandard deviationComplete blood countBlood filmMathematicsNuclear medicinePathologyStatisticsMedicineInternal medicineHematocritComputer scienceArtificial intelligenceAnemia

Abstract

fetched live from OpenAlex

The red blood cell distribution width (RDW), as part of an automated complete blood count (CBC), is a routinely available parameter on hematology analyzers. This parameter is the most commonly reported index of the variation in red cell volume and can be used to detect subtle degrees of anisocytosis. It is one of the most studied parameters; however, some earlier studies have shown overlap or discrepancy in the interpretations of these results. RDW is computed directly from the red blood cell (RBC) histogram and expressed as coefficient of variation (CV) or standard deviation (SD). In conjunction with other CBC parameters, such as the histogram, mean corpuscular volume (MCV), and peripheral blood film analysis, RDW is frequently used to interpret aberrations in red cell morphology. This article describes and discusses the different methods for measuring red blood cell dispersion and explains the reasons for the inconsistencies observed when interpreting RDW results.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.004
Science and technology studies0.0010.004
Scholarly communication0.0040.005
Open science0.0030.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.008
GPT teacher head0.241
Teacher spread0.233 · 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 source (direct Gemma or distilled Codex), 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

Citations66
Published2013
Admission routes1
Has abstractyes

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