Towards practical sample preparation in point-of-care testing: user-friendly microfluidic devices
Bibliographic record
Abstract
Microfluidic technologies offer a number of advantages for sample preparation in point-of-care testing (POCT), but the requirement for complicated external pumping systems limits their wide use. To facilitate sample preparation in POCT, various methods have been developed to operate microfluidic devices without complicated external pumping systems. In this review, we introduce an overview of user-friendly microfluidic devices for practical sample preparation in POCT, including self- and hand-operated microfluidic devices. Self-operated microfluidic devices exploit capillary force, vacuum-driven pressure, or gas-generating chemical reactions to apply pressure into microchannels, and hand-operated microfluidic devices utilize human power sources using simple equipment, including a syringe, pipette, or simply by using finger actuation. Furthermore, this review provides future perspectives to realize user-friendly integrated microfluidic circuits for wider applications with the integration of simple microfluidic valves.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".