Hemodialysis monitoring in whole blood using transmission and diffuse reflection spectroscopy: a pilot study
Bibliographic record
Abstract
Visible and near infrared transmission and diffuse reflection spectroscopy were used to monitor changes in whole blood resulting from hemodialysis treatment for end-stage renal disease. Blood samples from 8 patients on chronic hemodialysis therapy were measured in the 500- to 1700-nm wavelength range immediately before and after a single treatment. Principal component scores characteristic of each spectrum were derived, and mean pre- and posttreatment scores of the first principal component indicated a significant treatment-dependent change in both optical transmission (P = 0.004) and diffuse reflection (P < 0.001). Significant treatment-induced change persisted (P < 0.05) when the first four principal components were used to account for >97% of the treatment-dependent spectral variation. Some blood spectral changes expressed in terms of difference spectra (posttreatment - pretreatment) were consistent with standard clinical indicators of weight reduction, urea reduction, and potassium change, with probable origins at a molecular level. The results indicate the feasibility of using optical transmission and diffuse reflection spectroscopy to characterize clinically relevant blood changes for the future development of more comprehensive indicators of hemodialysis efficacy and long-term clinical outcomes. Moreover, the optical techniques employed are adaptable for potential online monitoring of blood changes during the hemodialysis treatment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".