Intraoperative Near-infrared Spectroscopy Can Predict Skin Flap Necrosis
Bibliographic record
Abstract
Background: The study aimed to validate the previously identified capacity of near-infrared spectroscopy (NIRS) to detect clinically relevant differences in tissue perfusion intraoperatively. Methods: Consecutive patients undergoing oncologic resection requiring flap reconstruction were analyzed. Clinicians were blinded to tissue oxygen saturation (StO2) measurements taken intraoperatively. Measurements were taken at (1) control areas not affected by the procedure, (2) areas at risk of necrosis based on distal location, and (3) areas of skin flap necrosis (SFN) identified during the follow-up period. Mean StO2 values were compared using a single-sample t test and analysis of variance (ANOVA) to determine differences in oxygenation. Results: There were 102 patients included from April 2018 to May 2019. Reconstruction was undertaken following resection for breast cancer (46), melanoma (35), sarcoma (9), and other cutaneous malignancies (12). Breast reconstruction involved 38 alloplastic reconstructions and eight autologous free flaps. Other skin flap reconstruction involved 42 local/regional skin flaps, 13 pedicled flaps, and one free flap. Eighteen patients (17.6%) developed SFN. Mean intraoperative StO2 measurements for control areas, areas at risk, and areas of SFN were 74.8%, 70.9%, and 54.3%, respectively. StO2 values equal to or less than 60% were highly specific (96%) for SFN, whereas StO2 values above 85% were highly sensitive (96%) to rule out SFN. Conclusion: These results further support the use of NIRS to objectively assess variations in skin flap oxygenation and tissue perfusion that are correlated with the development of postoperative SFN.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".