Infrared Spectroscopy in Clinical and Diagnostic Analysis
Bibliographic record
Abstract
Abstract The infrared spectrum of a mixture serves as the basis to quantitate its constituents, and a number of common clinical chemistry tests have proven to be feasible using this approach. This article reviews the infrared spectroscopy‐based analytical methods that have been developed for consideration as clinical assays, including serum analysis, urine analysis, amniotic fluid assays for the estimation of fetal lung maturity, and others. Because of the widespread interest in the potential for in vivo measurement of blood glucose using near‐infrared spectroscopy, a separate section is devoted to the analysis of glucose in whole blood. A related technique uses the infrared spectrum of biomedical specimens directly as a diagnostic tool. For example, the spectra of serum and of synovial fluid have proven to be useful in the diagnosis of metabolic disorders and arthritis, respectively, without explicitly recovering their chemical composition from the spectra. Rather, characteristic spectral features and patterns have been identified as the basis to distinguish spectra corresponding to healthy patients from those corresponding to diseased patients. These applications are reviewed here. Issues such as ease of use, speed, reliability, sample size, and calibration stability all play important roles in governing the practical acceptability of infrared spectroscopy‐based analytical methods. To provide a framework to illustrate these issues, descriptions are included for the various procedures that have been explored to wed successfully infrared spectroscopy to clinical chemistry.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.022 |
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".