Reply: Intraoperative Near-infrared Spectroscopy Can Predict Skin Flap Necrosis
Notice bibliographique
Résumé
Many thanks to the reviewers for their comments.1 Like most science, the devil is in the details, and we acknowledge that there may be differences in semantics. Nonetheless, the main findings from our study remain relevant in the utility of novel imaging modalities to assess tissue viability. The differentiation between near-infrared spectroscopy (NIRS) and near-infrared imaging is semantic in nature. The definition of spectroscopy is the measurement and interpretation of electromagnetic spectra. As SnapshotNIR is emitting and then measuring reflected near-infrared light spectra, it falls under this purview of spectroscopy. The depth of near-infrared light penetration is wavelength-dependent, or in some contact-based applications, dependent on the distance between the emitted signal to the sensor receiving the signal. As such, light penetration depth cannot be tied so specifically to imaging versus spectroscopy. Furthermore, the contact versus noncontact nature is not a defining feature of spectroscopy. Indeed, some of the earliest applications of NIRS came from the agriculture industry to assess properties of crops in a noncontact way.2 As such, although SnapshotNIR is a noncontinuous and noncontact assessment of tissue oxygenation, this does not make it any less of a spectroscopy device. We would argue that near-infrared imaging may be a subset of NIRS devices, and the 2 are not mutually exclusive. Many of the other peer-reviewed publications using the same technology have preferentially used the term NIRS.3–6 Additionally, there are reports using near-infrared imaging and NIRS interchangeably which calls into question what the critical distinction is between these 2 terms.7 We also appreciate the feedback regarding the distinction of measuring perfusion verses measuring oxygenation. The NIRS device used provides a direct measure of oxygenated hemoglobin. Although this is not a direct measure of perfusion, it is a very good surrogate measure of perfusion and metabolism, especially in the clinical cases presented in this study. Although oxygenation and perfusion are linked (through the Fick equation), they are different. Oxygen consumption (V̇O2) by tissue is directly tied to the tissue blood flow (Q̇tissue) and the arteriovenous difference in oxygen content, as demonstrated in the following formula: V˙O2tissue=Q˙tissue×(CaO2−CvO2) Assuming standard surgical conditions (normal respiratory function and minimal arterial-venous differences in partial pressure of oxygen and hemoglobin) a patient’s arterial saturation will be ~98%, causing measurements of tissue oxygen saturation (StO2) to be almost exclusively contingent on changes in venous blood volume and venous oxygen saturation. As such, this equation can be adapted to the following: V˙O2tissue=Q˙tissue×(98%−StO2)%Volv As SnapshotNIR provides us spatial resolution of both control tissue and tissue at risk within a single set of images, we can be relatively confident that metabolism is conserved in like tissue, and so, changes in StO2 are reflective of changes in blood flow. Importantly, we are not measuring StO2 on areas where we feel it likely that perfusion and oxygen consumption have been decoupled (diffusion limitation or mitochondrial dysfunction). Furthermore, SnapshotNIR provides relative intensities of total hemoglobin, which is a surrogate measure of how much blood volume is within a particular area (data not presented in our work). Although they are not true measures of perfusion, they are implicit markers of blood flow and are likely not affected in our particular application. DISCLOSURE The author has no financial interest to declare in relation to the content of this article.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».