Clinical utility of Raman spectroscopy: current applications and ongoing developments
Bibliographic record
Abstract
Hanna C McGregor,1 Wenbo Wang,1,2 Michael A Short,1 Haishan Zeng,1,3 1Integrative Oncology Department, BC Cancer Agency Research Centre, 2Department of Biomedical Engineering, 3Department of Dermatology, The University of British Columbia, Vancouver, BC, Canada Abstract: Availability of fast, noninvasive/minimally invasive, and accurate diagnostic tests can maximize the benefit of patient care. The application of Raman spectroscopy (RS) in biological and biomedical applications has surged recently as a result of technological advancements in instrumentation and spectral data handling techniques. With maturation, the potential of RS in clinical diagnosis of various diseases, in particular, early cancer, has been widely explored and reported. This paper provides an introduction to the Raman theory and technology behind RS for nonspecialists interested in its clinical uses. Latest achievements in oncological, cardiovascular, and neurological applications of RS along with its clinical implementations are discussed. Keywords: Biomedical optics, clinical diagnosis, early detection, cancer, cardiology, neurology
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".