Point-of-Care Tissue Oxygenation Assessment with SnapshotNIR for Alloplastic and Autologous Breast Reconstruction
Bibliographic record
Abstract
Background: In breast reconstruction, mastectomy and free flaps are susceptible to vascular compromise and tissue necrosis. The SnapshotNIR device (Kent Imaging, Calgary, AB, Canada) utilizes near-infrared spectroscopy to measure tissue oxygen saturation (StO2) and hemoglobin concentration. Here, we report on the use of this device for StO2 monitoring among patients receiving alloplastic or autologous breast reconstruction. Methods: Patients receiving immediate alloplastic reconstruction after mastectomy or autologous reconstruction were enrolled. Preoperative, intraoperative, and postoperative images were taken of the flaps. StO2 and hemoglobin were measured at the following locations: superior and inferior breast, free flap skin paddle (when applicable), and un-operated control skin. Linear mixed effects model for repeated measurements was used to model measurements to estimate the area effect difference across time, time effect difference across area, and pairwise comparisons between two areas at each time point. Results: Thirty-two breasts underwent alloplastic reconstruction; 38 breasts underwent autologous reconstruction. No enrollees developed skin necrosis. StO2 was highest after mastectomy and closure in alloplastic reconstructions. StO2 was observed to decline at follow-up in autologous reconstructions. Mean preoperative StO2 was highest in breasts that had previously undergone mastectomy and alloplastic reconstruction. Conclusions: The SnapshotNIR device detected normal spatial and temporal differences in tissue oxygenation over the operative course of alloplastic and autologous breast reconstruction. A multi-institutional, prospective clinical trial is needed to determine the sensitivity and specificity of this device for detecting skin flap necrosis.
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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 | 0.002 |
| 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".