Clinical applications of infrared and Raman spectroscopy: state of play and future challenges
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Vibrational spectroscopies, based on infrared absorption and/or Raman scattering provide a detailed fingerprint of a material, based on the chemical content. Diagnostic and prognostic tools based on these technologies have the potential to revolutionise our clinical systems leading to improved patient outcome, more efficient public services and significant economic savings. However, despite these strong drivers, there are many fundamental scientific and technological challenges which have limited the implementation of this technology in the clinical arena, although recent years have seen significant progress in addressing these challenges. This review examines (i) the state of the art of clinical applications of infrared absorption and Raman spectroscopy, and (ii) the outstanding challenges, and progress towards translation, highlighting specific examples in the areas of in vivo, ex vivo and in vitro applications. In addition, the requirements of instrumentation suitable for use in the clinic, strategies for pre-processing and statistical analysis in clinical spectroscopy and data sharing protocols, will be discussed. Emerging consensus recommendations are presented, and the future perspectives of the field are assessed, particularly in the context of national and international collaborative research initiatives, such as the UK EPSRC Clinical Infrared and Raman Spectroscopy Network, the EU COST Action Raman4Clinics, and the International Society for Clinical Spectroscopy.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it