Pre‐cut dried blood spot (PCDBS): an alternative to dried blood spot (DBS) technique to overcome hematocrit impact
Bibliographic record
Abstract
Quantification of analytes by Dried Blood Spots (DBS) on different paper cards has been extensively reported in the past several years. However, some factors limit the robustness of the precision and accuracy of DBS such as: hematocrit level, blood viscosity, analyte nature, spotting technique and spotting conditions. As such, the paper material used for DBS must meet strict quality control criteria to produce reliable quantification of drugs: uniformity, no chemical leaching and no chromatographic effect. To overcome these variables, especially the hematocrit impact, a modification of the traditional DBS, named Pre-Cut Dried Blood Spot (PCDBS), is presented. In contrast to the classical DBS technique, the new PCDBS procedure demonstrates no variation in response, within ±3%, independently of the hematocrit level or of the type of card used. The impact of the hematocrit level on the analyte recovery is discussed for both DBS and PCDBS approaches. Moreover, for quantification of naproxen by liquid chromatography/electrospray ionization tandem mass spectrometry (LC/ESI-MS/MS), the PCDBS technique was demonstrated to be as precise (%CV ≤3.1%) and accurate (%nominal between 95.4 and 104.4%) as the classical DBS procedure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".