Emerging Evidence and Critical Issues with the Use of Single-Drop Capillary Blood for the Measurement of Hemoglobin Concentration in Population-Level Anemia Surveys
Bibliographic record
Abstract
Accurate and precise measurement of hemoglobin concentration is critical for reliable estimations of anemia prevalence at the population level. When systematic and/or random error are introduced in hemoglobin measurement, estimates of anemia prevalence might be significantly erroneous and, hence, limit their usefulness. For decades, single-drop capillary blood has been the most common blood source used for the measurement of hemoglobin concentration in surveys, especially in low-income and middle-income countries. In this study, we highlight historical and emerging evidence that single-drop capillary blood introduces a high degree of random error (variability) to hemoglobin estimates, leading to less reliable estimates of anemia prevalence at the population level. At present, the best practice is to collect and use venous blood for measurement of hemoglobin with an automated hematology analyzer, following standard operating procedures and quality assurance measures. Where use of an automated analyzer is not possible, the analysis of venous blood in a point-of-care hemoglobinometer by trained phlebotomists or specimen collectors should be considered. A forthcoming systematic review will provide additional evidence on the accuracy and precision of single-drop capillary blood for hemoglobin assessment. In the meantime, we raise caution when using single-drop capillary blood for hemoglobin measurement as it can result in inaccurate hemoglobin estimates and less reliable anemia prevalence estimates.
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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 | 0.032 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
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".