An Evaluation of Two Methods to Measure Hemoglobin Concentration among Women with Genetic Hemoglobin Disorders in Cambodia: A Method‐Comparison Study
Bibliographic record
Abstract
Genetic hemoglobin (Hb) E variants are common in Cambodia and result in an altered and unstable Hb molecule. There are no known studies on the accuracy of Hb measurement using these methods in individuals with Hb E variants. Methods We measured Hb concentration in capillary blood using a hemoglobinometer (HemoCue) and in venous blood using a hematology analyzer (Sysmex XT‐1800i) in 420 Cambodian women (18‐45 y). Results Bias and concordance appeared similar between methods among women with no Hb disorders (n=195, bias=2.5, p c =0.68), women with Hb E variants (n=133, bias=2.5, p c =0.78), and women with other Hb variants (n=92, bias=2.7, p c =0.73). The overall bias (difference in Hb means between methods) was 2.6 g/L , resulting in a difference in anemia prevalence of 11.5% (Hemocue 41% vs. Sysmex 29.5%, p <0.001). Based on concordance plots, the HemoCue device appears to underestimate Hb concentrations in capillary blood as compared to Sysmex (venous blood) at lower Hb concentrations, and to overestimate Hb concentrations in capillary blood as compared to Sysmex at higher Hb concentrations. Conclusions Bias and concordance were similar among all groups, suggesting the two methods were comparable in measuring Hb among women in all groups. We highlight the bias between the two methods to caution programming staff, researchers, and policy makers in the interpretation of data and the impact that even a small bias can have on anemia prevalence rates.
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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 | 0.034 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".