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Record W2563046280 · doi:10.1111/ijlh.12583

A method comparison study between two hemoglobinometer models (Hemocue Hb 301 and Hb 201+) to measure hemoglobin concentrations and estimate anemia prevalence among women in Preah Vihear, Cambodia

2016· article· en· W2563046280 on OpenAlexaff
Aviva I Rappaport, Crystal D Karakochuk, Kyly C. Whitfield, Khin Meng Kheang, Tim Green

Bibliographic record

VenueInternational Journal of Laboratory Hematology · 2016
Typearticle
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsConcordanceHemoglobinMedicineAnemiaLimits of agreementPopulationDemographyPediatricsInternal medicineNuclear medicineEnvironmental health

Abstract

fetched live from OpenAlex

INTRODUCTION: Hemoglobin (Hb) concentration is often measured in global health and nutrition surveys to determine anemia prevalence using a portable hemoglobinometer such as the Hemocue® Hb 201+. More recently, a newer model was released (Hemocue Hb 301) utilizing slightly different methods to measure Hb as compared to the older model. The objective was to measure bias and concordance between Hb concentrations using the Hemocue Hb 301 and Hb 201+ models in a rural field setting. METHODS: Hemoglobin (Hb) concentration was measured using one finger prick of blood (approximately 10 μL) from 175 Cambodian women (18-49 years) using three Hemocue Hb 201+ and three Hb 301 machines. Bias and concordance were measured and plotted. RESULTS: Overall, mean ± SD Hb concentration was 116 ± 13 g/L using the Hb 201+ and 118 ± 12 g/L using the Hb 301; and anemia prevalence (Hb < 120 g/L) was 58% (n = 102) and 58% (n = 101), respectively. Overall bias ± SD was 2.0 ± 10.5 g/L and concordance (95% CI) was 0.63 (0.54, 0.72). Despite the 2 g/L bias detected between models, anemia prevalence was very similar in both models. CONCLUSIONS: The two models measured anemia prevalence comparably in this population of women in rural Cambodia.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.024
Threshold uncertainty score0.697

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.022
GPT teacher head0.350
Teacher spread0.328 · 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

Citations25
Published2016
Admission routes1
Has abstractyes

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