Optical monitoring of transplanted free flaps using an implantable near-infrared spectroscopy sensor
Bibliographic record
Abstract
Free tissue transfer (FTT) is a surgical procedure that involves taking tissue from one area of the body and transplanting it to a surgical wound. Near-infrared spectroscopy (NIRS) has the potential to provide continuous and non-invasive monitoring of FTT hemodynamics. A novel NIRS system with a miniaturized implantable sensor was developed for FTT monitoring in head and neck surgery. The objectives of this study were to obtain post-operative NIRS measurements on a cohort of patients undergoing FTT surgery for head and neck cancer and to evaluate the patient’s and clinician’s experience with the novel NIRS monitoring method. The NIRS sensor was fixed over the FTT for 72 hours post-operatively to provide tissue oxygenation parameters, including oxygenated (O2Hb), deoxygenated (HHb), and tissue saturation index (TSI). After 72 hours, the patient and clinicians completed a questionnaire to evaluate their experience with the NIRS system. All patients undergoing FTT surgery had a successful operation with no complications to the FTT. The NIRS data showed visible pulsatile O2Hb signals, indicating proper microvascular function of the FTT. Furthermore, TSI calculations provided an absolute measure of the oxygenation status of the FTT. The questionnaire indicated that the NIRS sensor did not cause additional discomfort or inconvenience to the patients or clinicians. Our results suggest that the novel NIRS sensor can monitor the FTT continuously and non-invasively for 72 hours with minimal interference to patient care. Incorporating a novel NIRS biosensor into FTT monitoring can improve post-operative care and decrease FTT failure rates.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".