Screening for Thalassemia Carriers in Populations with a High Rate of Iron Deficiency: Revisiting the Applicability of the Mentzer Index and the Effect of Iron Deficiency on Hb A<sub>2</sub>Levels
Bibliographic record
Abstract
Differentiating between β-thalassemia (β-thal) minor and iron deficiency has important implications in thalassemia carrier screening. Several complete blood count (CBC)-based equations have been proposed for differentiating these two conditions. The applicability of these equations in populations with high rates of iron deficiency and β-thal minor, where patients can have both conditions, is limited. In addition, there have been conflicting reports on the possible effect of iron deficiency on Hb A2 level with possible consequences for thalassemia screening programs. Here, we demonstrate that in our population the Mentzer Index separates individuals with β-thal minor from those without β-thal minor, regardless of their iron status. Iron deficiency also does not reduce Hb A2 levels in β-thal minor patients. Correction of iron deficiency is not required for diagnosis of β-thal minor using high performance liquid chromatography (HPLC).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".