Evaluation of low-cost techniques to detect sickle cell disease and β-thalassemia: an open-label, international, multicentre study
Bibliographic record
Abstract
Background: Sickle cell disease (SCD) persists as a major global health problem, disproportionately affecting children in low- and middle-income countries (LMIC). Accurate and low-cost point-of-care techniques are urgently needed in LMIC to detect carrier or disease forms with haemoglobin S (HbS) and other variants like β-thalassemia. Methods: An open-label, international, multicentre study was conducted at clinical sites in Nepal and Canada. Blood samples were collected from healthy volunteers (HbAA) and participants with known haemoglobinopathies (HbA/β-thalassemia, HbAS, HbS/β-thalassemia, HbSS). The performance of six low-cost tests (Conventional sickling test; HbS solubility test; HemoTypeSC; Sickle SCAN; Gazelle Hb variant test; Automated sickling test using automated microscopy and machine learning) was evaluated against HPLC (ClinicalTrials.gov Identifier: NCT05506358). Findings: Between September 2022 and March 2023, we enrolled 138 participants (aged 2-74 years; 59% female, 41% male) at clinical sites in Nepal and Canada. Four low-cost tests (HemoTypeSC, Sickle SCAN, Gazelle, and automated sickling), which could identify phenotypes, detected severe SCD (HbSS, HbS/β-thalassemia) accurately (sensitivity >96%; specificity >99%). In contrast, for carrier forms, HemotypeSC and Sickle SCAN only detected HbAS (sensitivity >97%; specificity 100%) and not HbA/β-thalassemia (sensitivity 0%; specificity 100%), while Gazelle detected HbAS (sensitivity 100%, specificity 100%) and HbA/β-thalassemia (sensitivity 91%, specificity 99%), and automated sickling test detected both trait conditions (HbAS and HbA/β-thalassemia; sensitivity 85%, specificity 85%). Interpretation: When HbS co-exists with β-thalassemia, Gazelle and automated sickling test accurately identify severe SCD and carrier forms. However, HemotypeSC and Sickle SCAN miss β-thalassemia trait, and need to be complemented with other low-cost tests. Funding: UBCPSI, Canada Research Chairs, UBC HIFI Awards, UBC 4YF, Naiman Vickars Endowment fund.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".