Novel Clinical Near-Infrared Spectroscopy Sensor in Flap Monitoring
Bibliographic record
Abstract
Head and neck cancer ranks as the seventh most prevalent cancer worldwide, often treated through free tissue transfer (FTT)—a complex surgical procedure where tissue with its blood supply is transplanted to reconstruct areas affected by tumour removal. Postoperative vascular compromise jeopardizes flap viability, necessitating rapid and accurate monitoring. Traditional hourly clinical checks are subjective and invasive, underscoring the need for non-invasive, continuous monitoring methods. This study evaluates a novel Near-Infrared Spectroscopy (NIRS) sensor system, specifically designed for FTT applications, capable of detecting venous and arterial occlusions by measuring the Total Oxygenation Index (TOI). The compact sensor ($31 \times 16$mm) employs multi-wavelength with two LEDs (730 nm, 810 nm, and 850 nm) and a photodetector, effectively penetrating tissues up to 20 mm depth to monitor hemodynamic changes. Experimental results from clinical trials showed a distinct 0.5% initial increase in TOI during venous occlusion, followed by a return to baseline. Arterial occlusion led to a notable 2% decrease in TOI within five minutes, with a rapid recovery of 2% in less than one minute post-occlusion. The sensor accurately captured critical physiological signals, including cardiac pulsation and respiratory cycles. The findings highlight the technical reliability, clinical feasibility, and high sensitivity of TOI-based NIRS monitoring, underscoring its significant potential to enhance vascular compromise detection, improve flap salvage rates, and reduce postoperative morbidity.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".