Emerging point-of-care technologies for sickle cell disease screening and monitoring
Bibliographic record
Abstract
INTRODUCTION: Sickle Cell Disease (SCD) affects 100,000 Americans and more than 14 million people globally, mostly in economically disadvantaged populations, and requires early diagnosis after birth and constant monitoring throughout the life-span of the patient. Areas covered: Early diagnosis of SCD still remains a challenge in preventing childhood mortality in the developing world due to requirements of skilled personnel and high-cost of currently available modalities. On the other hand, SCD monitoring presents insurmountable challenges due to heterogeneities among patient populations, as well as in the same individual longitudinally. Here, we describe emerging point-of-care micro/nano platform technologies for SCD screening and monitoring, and critically discuss current state of the art, potential challenges associated with these technologies, and future directions. Expert commentary: Recently developed microtechnologies offer simple, rapid, and affordable screening of SCD and have the potential to facilitate universal screening in resource-limited settings and developing countries. On the other hand, monitoring of SCD is more complicated compared to diagnosis and requires comprehensive validation of efficacy. Early use of novel microdevices for patient monitoring might come in especially handy in new clinical trial designs of emerging therapies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".