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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.061 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".