Variability in haemoglobin concentration by measurement tool and blood source: an analysis from seven countries
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Bibliographic record
Abstract
OBJECTIVE: We explore factors such as the blood sampling site (capillary vs venous), the equipment (HemoCue vs automated haematology analyser) and the model of the HemoCue device (201+ vs 301) that may impact haemoglobin measurements in capillary and venous blood. METHODS: Eleven studies were identified, and bias, concordance and measures of diagnostic performance were assessed within each study. FINDINGS: Our analysis included 11 studies from seven countries (Cambodia, India, The Gambia, Ghana, Laos, Rwanda and USA). Samples came from children, men, non-pregnant women and pregnant women. Mean bias ranged from -8.7 to 2.5 g/L in Cambodian women, 6.2 g/L in Laotian children, 2.4 g/L in Ghanaian women, 0.8 g/L in Gambian children 6-23 months and 1.4 g/L in Rwandan children 6-59 months when comparing capillary blood on a HemoCue to venous blood on a haematology analyser. Bias was 8.3 g/L in Indian non-pregnant women and 2.6 g/L in Laotian children and women and 1.5 g/L in the US population when comparing capillary to venous blood using a HemoCue. For venous blood measured on the HemoCue compared with the automated haematology analyser, bias was 5.3 g/L in Gambian pregnant women 18-45 years and 11.3 g/L in Laotian children 6-59 months. CONCLUSION: Our analysis found large variability in haemoglobin concentration measured on capillary or venous blood and using HemoCue Hb 201+ or Hb 301 or automated haematology analyser. We cannot ascertain whether the variation is due to differences in the equipment, differences in capillary and venous blood, or factors affecting blood collection techniques.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it