Near‐Infrared Spectroscopy, In Vivo Tissue Analysis by
Bibliographic record
Abstract
Abstract In vivo near‐infrared (NIR) spectroscopy has the potential of becoming an important tool in a number of areas in clinical medicine. Technological developments in photonics that have been spurred on by the communication revolution have set the stage for rapid advancement of optical and NIR spectroscopy based on noninvasive or minimally invasive medical diagnostic techniques. The goal of this article is to review the current capabilities and limitations of in vivo NIR spectroscopy and highlight the impact of these capabilities and limitations in selected areas where NIR spectroscopy is being used to address clinical problems. The optical properties of tissues are briefly reviewed, as are the instrumental methods available to the experimentalist. These properties and methods largely dictate the feasibility of an in vivo spectroscopic diagnostic approach and constrain the scope of problems that can be tackled using optical–NIR spectroscopy. Some of the more successful applications are described, including studies of tissue oxygenation, ischemia, and viability. A number of factors that can confound interpretation of in vivo NIR results are discussed. The number and magnitude of confounding influences that arise in in vivo spectroscopy can be daunting to the experimentalist and may represent the largest barrier in transforming in vivo spectroscopic measurements into clinically meaningful and reliable information. In vivo NIR spectroscopy abounds with opportunity and challenge.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".