Raman spectroscopy for clinical-level detection of heparin in serum by partial least-squares analysis
Bibliographic record
Abstract
Heparin is the most widely used anti-coagulant for the prevention of blood clots in patients undergoing certain types of surgeries including open heart surgeries and dialysis. The precise monitoring of heparin amount in patients' blood is crucial for reducing the morbidity and mortality in surgical environments. Based upon these considerations, we have used Raman spectroscopy in conjunction with partial least squares (PLS) analysis to measure heparin concentration at clinical level which is less than 10 United States Pharmacopeia (USP) in serum. The PLS calibration model was constructed from the Raman spectra of different concentrations of heparin in serum. It showed a high coefficient of determination (R2>0.91) between the spectral data and heparin level in serum along with a low root mean square error of prediction ~4 USP/ml. It enabled the detection of extremely low concentrations of heparin in serum (~8 USP/ml) as desirable in clinical environment. The proposed optical method has the potential of being implemented as the point-of-care testing procedure during surgeries, where the interest is to rapidly monitor low concentrations of heparin in patient's blood.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".