Tissue Oxygenation Sensor and an Active<i>In Vitro</i>Phantom for Sensor Validation
Bibliographic record
Abstract
A free flap is a tissue reconstruction procedure where healthy tissue is harvested to cover up vital structures after wound debridement. Microvascular anastomoses are carried out to join the arteries and veins of the flap with recipient vessels near the target site. Continuous monitoring is required to identify the flap failure and enable early intervention to salvage the flap. Although there are medical instruments that can assist surgeons in monitoring flap viability, high upfront costs and time-consuming data interpretation have hindered the use of such technologies in practice. Surgeons still rely largely on the clinical examination to monitor flaps after operations. This paper presents a low-cost, low-power (6.6 mW), and miniaturized Hamlyn StO2(tissue oxygen saturation) sensor that can be embodied as a plaster and attached to a flap for real-time monitoring. Similar to the design of oxygen saturation (SpO2/SaO2) sensors, the Hamlyn StO2sensor was designed based on photoplethysmography (PPG), but with a different target of quantifying tissue perfusion rather than capturing pulsatile flow. To understand the spectral response to oxygenation/deoxygenation and vascular flow, an active in vitro silicone phantom was developed. The new sensor was validated using the silicone phantom and compared with a commercially available photospectroscopy and laser Doppler machine (O2C, LEA, Germany). In addition, in vivo experiments were conducted using a brachial pressure cuff forearm ischemia model. The experiment results have shown a high correlation between the proposed sensor and the O2C machine (r = 0.672 and p <; 0.001), demonstrating the potential value of the of the proposed low-cost sensor in post-operative free flap monitoring.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".