Point-of-care diagnostic test accuracy in children and adolescents with sickle cell disease: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Detection of sickle cell disease (SCD) could be improved with greater use of point-of-care testing (POCT). This review assessed the accuracy of POCTs for SCD in children and adolescents. METHODS: We systematically searched EMBASE, PubMed, Cochrane libraries, registries and conference proceedings from inception to 28th February 2023. We included cross-sectional and case-control studies that tested for SCD using POCTs and reference tests in individuals aged 0-19. We conducted meta-analysis to assess sensitivity and specificity of individual POCTs. FINDINGS: The review included 31 studies overall, with 20 covering lateral flow immunoassays (LFIAs) and four covering micro-engineered electrophoresis. When detecting homozygous SCD, the pooled sensitivity and specificity of the included LFIAs and micro-engineered electrophoresis POCTs was 92 % or higher in all individual meta-analyses. Sensitivities and specificities were also nearly 100 % when detecting haemoglobin SC disease for these POCTs. INTERPRETATION: POCTs could be used to accurately diagnose SCD.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.015 | 0.002 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".