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 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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".