Exploring phlebotomy technique as a pre-analytical factor in proteomic analyses by mass spectrometry
Bibliographic record
Abstract
Multiple reaction monitoring mass spectrometry (MRM-MS) is an emerging technology for blood biomarker verification and validation; however, the results may be influenced by pre-analytical factors. This exploratory study was designed to determine if differences in phlebotomy techniques would significantly affect the abundance of plasma proteins in an upcoming biomarker development study. Blood was drawn from 10 healthy participants using four techniques: (1) a 20-gauge IV with vacutainer, (2) a 21-gauge direct vacutainer, (3) an 18-gauge butterfly with vacutainer, and (4) an 18-gauge butterfly with syringe draw. The abundances of a panel of 122 proteins (117 proteins, plus 5 matrix metalloproteinase (MMP) proteins) were targeted by LC/MRM-MS. In addition, complete blood count (CBC) data were also compared across the four techniques. Phlebotomy technique significantly affected 2 of the 11 CBC parameters (red blood cell count, p = 0.010; hemoglobin concentration, p = 0.035) and only 12 of the targeted 117 proteins (p < 0.05). Of the five MMP proteins, only MMP7 was detectable and its concentration was not significantly affected by different techniques. Overall, most proteins in this exploratory study were not significantly influenced by phlebotomy technique; however, a larger study with additional patients will be required for confirmation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".