Measurement and interpretation of hemoglobin concentration in clinical and field settings: a narrative review
Bibliographic record
Abstract
Anemia affects over 800 million women and children globally. Defined as a limited or insufficient functional red blood cell supply in peripheral blood, anemia causes a reduced oxygen supply to tissues and can have serious health consequences for women and children. Hemoglobin (Hb) concentration is most commonly measured for anemia diagnosis. Methods to measure Hb are usually invasive (requiring a blood sample); however, advances in diagnostic and clinical chemistry over the past decade have led to the development of new noninvasive methods. Accurate diagnosis at the individual level is important to identify individuals who require treatment. At the population level, anemia prevalence estimates are often the impetus for national nutrition policies or programs. Thus, it is essential that methods for Hb measurement are sensitive, specific, accurate, and reproducible. The objective of our narrative review is to describe the basic principles, advantages, limitations, and quality control issues related to methods of Hb measurement in clinical and field settings. We also discuss other biomarkers and tests that can help to determine the severity and underlying causes of anemia. In conclusion, there are many established and emerging methods to measure Hb concentration, each with their own advantages, limitations, and factors to consider before use.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".