Integration of paper-based analytical devices with digital microfluidics for colorimetric detection of creatinine
Bibliographic record
Abstract
DMF-PAD and the spectrophotometric reference method. The concentrations of CR determined using both analytical techniques were close to the theoretical values, with the resultant standard deviations of 2-9% and 1-4% for DMF-PADs and spectrophotometry, respectively. Furthermore, the recovery values were within the acceptable range, with DMF-PADs yielding 96-108% and spectrophotometry producing 95-102%. Finally, the greenness of the DMF-PAD and spectrophotometry methods was evaluated using the Analytical Greenness (AGREE) metric software, in which 0.71 and 0.51 scores were obtained, respectively. This indicates that the proposed method presents a higher greenness level, mainly due to its miniaturized characteristics using a smaller volume of reagent and sample and the possibility of automation, thus reducing user exposure to potentially toxic substances. Therefore, the DMF-PADs demonstrated great potential for application in the clinical analysis of creatinine, aiding in routine tests by introducing an automated, simple, and environmentally friendly process.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".