An Algorithm to Aid in the Investigation of Thalassemia Trait in Multicultural Populations
Bibliographic record
Abstract
CONTEXT: The differentiation between iron deficiency and a thalassemia syndrome is an important consideration in the investigation of microcytic anemia. OBJECTIVE: An established statistical method was used to demonstrate the importance of considering ethnic background in combination with mean cell volume (MCV) in the investigation of beta-thalassemia trait in a multicultural urban population. DESIGN: Posttest probabilities for beta-thalassemia trait were calculated using likelihood ratios for various microcytic MCV ranges in conjunction with published pretest probabilities for beta-thalassemia trait based on ethnic background. SETTING: Regional hemoglobinopathy laboratory, St Joseph's Hospital, Hamilton, Ontario, Canada. PATIENTS: Patient data were derived from a previously published study. The original study cohort consisted of 789 patients aged 18 years or older who had an MCV less than 80 fL and were referred for routine complete blood count during a 6-month period. MAIN OUTCOME MEASURES: Posttest probabilities. RESULTS: Simplified tables for the determination of posttest probabilities for beta-thalassemia trait in individual patients based on ethnic background and MCV are provided. An algorithm to assist in determining when thalassemia investigations are indicated is presented. CONCLUSIONS: A high index of suspicion based on ethnic background and low MCV can provide increased sensitivity and specificity for the detection of thalassemia trait in centers with multicultural populations similar to the study population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".