Intraoperative Near-infrared Spectroscopy Can Predict Skin Flap Necrosis
Bibliographic record
Abstract
Dear sir, We have carefully reviewed with interest the article titled “Intraoperative Near-infrared Spectroscopy Can Predict Skin Flap Necrosis” by Hill et al,1 published in Plastic and Reconstructive Surgery Global Open on March 26, 2024. We acknowledge the importance of investigating a new noninvasive optical technology for early detection of skin flap necrosis, supported by a substantial sample size. We also appreciate the complexities involved in conducting such a study, as we are familiar with the optical technology used in this study and its utilization for the intraoperative assessment of tissue oxygenation.2 Although we found this study engaging and important, we would like to offer some comments and observations. We hope that these observations can be addressed, as the article has the potential to make a valuable contribution to the literature. The technology used in this study has been misinterpreted. Specifically, Snapshot-NIR should be classified as a near-infrared imaging (NIRI) technique rather than a near-infrared spectroscopy (NIRS) technique. Although both methodologies are based on the same principles of tissue optics, they are different techniques. NIRI, also known as diffuse optical imaging or topography, is a noncontact technique that assesses regional tissue oxygenation (StO2) in superficial layers (up to 2 mm) by calculating NIR light reflection and reconstructing two-dimensional images of the chromophore concentrations in tissue. In contrast, NIRS is a contact technique that evaluates changes in tissue StO2 in deeper layers (up to 20 mm) by calculating NIR light absorption by tissue chromophores.3 Therefore, although we concur with the article’s title indicating that intraoperative NIRS can predict skin flap necrosis, the study utilizes NIRI as the technique.4 The article describes Snapshot as an NIRS device that measures tissue perfusion, which is not an accurate definition. NIRI and NIRS techniques can only measure changes in tissue oxygenation, not tissue perfusion. Tissue perfusion refers to the delivery of blood to a capillary bed in tissue, whereas tissue oxygenation indicates the status of oxygen consumption by tissue cells.5 Although tissue oxygenation is often a good surrogate of tissue perfusion, it may not always hold true, especially in pathological conditions and injured tissues where the correlation between the two parameters may be inconsistent. For example, a flap may have good microvascular blood supply, but the cells within the flap may not be able to efficiently utilize oxygen due to cell damage. In scenarios like these, which are not uncommon in transplant surgery, local perfusion may be sufficient, but tissue oxygenation could be inadequate, potentially compromising tissue viability. Understanding the differences and relationship between tissue perfusion and oxygenation in pathological conditions is highly important, especially in reconstructive surgery, where the use of NIRS and NIRI techniques is increasingly improving the monitoring of flap vitality and function. In reconstructive surgery, postsurgical care requires reliable techniques for monitoring the hemodynamics and metabolic conditions of reconstructed tissue. This necessitates real-time, continuous monitoring of both local tissue perfusion and oxygenation independently. We hope addressing these points will enhance the precision and coherence of this valuable article. Thank you for considering our letter. DISCLOSURE The authors received a Research Project Grant from the Canadian Institute of Health Research to conduct research titled “Advanced optical monitoring of free tissue transfer hemodynamics” (Application Number: 497249) in 2023.
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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 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".