Advancing chronic wound care with near-infrared spectroscopy imaging: clinical applications, measurement parameters, and insights into healing dynamics.
Bibliographic record
Abstract
BACKGROUND: Chronic wound management is a global health care challenge affecting patient morbidity and quality of life while presenting a substantial economic burden. A critical limitation in effective wound care is the inability to accurately assess microvascular tissue health in real time. This review of near-infrared spectroscopy (NIRS) imaging and its use in wound care emphasizes relevant clinical end points and explores key measurement parameters assessed via NIRS imaging. OBJECTIVE: To identify, describe, and illustrate NIRS imaging modalities and measurement parameters, and their clinical applications. RESULTS: Clinical studies demonstrated that NIRS imaging can effectively detect poor wound healing early, facilitating timely interventions. Changes in parameters such as oxygenated hemoglobin, deoxygenated hemoglobin, and tissue oxygen saturation have shown strong correlations with wound healing progress, enabling clinicians to make more informed decisions. CONCLUSION: NIRS imaging advances wound management by providing real-time, noninvasive, and objective data on tissue oxygenation and perfusion. NIRS imaging may objectively complement the standard percentage area reduction assessments during the wound treatment process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".