Can Automated Hematology Analyzers Predict the Presence of a Genetic Hemoglobinopathy? An Analysis of Hematological Biomarkers in Cambodian Women
Bibliographic record
Abstract
Genetic hemoglobinopathies are the most common single-gene disorder worldwide. Some automated hematology analyzers have the capability of flagging individuals who may have hematological disorders based on complete blood count (CBC) biomarkers. We aimed to evaluate the accuracy of a hematology analyzer in identifying genetic hemoglobinopathies in Cambodian women and to determine which hematological biomarkers are the best predictors. A CBC was completed using a Sysmex XN-1000 analyzer and hemoglobinopathies were determined with capillary hemoglobin electrophoresis for 808 nonpregnant Cambodian women. Sysmex XN-1000 Interpretive Program (IP) messages, which flag potential hematological disorders, were produced from CBC results. Then, 2 × 2 tables were used to determine sensitivity and specificity of the IP message “Hemoglobin defect” to detect a genetic hemoglobinopathy. Receiver operating characteristic (ROC) analyses assessed the diagnostic ability of six CBC biomarkers to predict a genetic hemoglobinopathy. In total, 74% of women had a hemoglobinopathy (predominantly Hb E and α-thalassemia). “Hb defect” IP message sensitivity and specificity for genetic hemoglobinopathy detection were 10.4% and 98.6%, respectively. Variable selection strategies yielded a two-variable model including mean corpuscular volume (MCV) and red blood cell (RBC) count (AIC = 99.83, AUCROC = 0.98 (95% CI: 0.97, 0.99)) for the prediction of a homozygous EE disorder. Sensitivity and specificity values do not justify the use of Sysmex XN-1000 IP flag messages for identification of genetic hemoglobinopathies in Cambodian women. Development of an algorithm based on MCV and RBC biomarkers may optimize the screening ability of automated hematology analyzers.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".