Evaluating SnapshotNIR for Tissue Oxygenation Measurement Across Skin Types After Mastectomy
Bibliographic record
Abstract
Accurate monitoring of mastectomy skin flap (MSF) perfusion is critical, especially in patients with darker skin pigmentation at higher risk of misdiagnosed tissue ischemia. Near-infrared spectroscopy (NIRS) devices, such as SnapshotNIR, offer real-time tissue oxygen saturation measurements (StO2), but their accuracy across skin pigmentation levels remains unexplored. This quasi-experimental study included 33 patients undergoing mastectomy. MSF edge ΔStO2, defined as preoperative minus postoperative StO2, was measured using SnapshotNIR device (Kent Imaging, Calgary, AB, Canada) pre- and post-mastectomy. By definition, a positive ΔStO2 indicates a decrease in tissue oxygenation, while a negative ΔStO2 indicates an increase relative to baseline. ΔStO2 was analyzed against Fitzpatrick scores to assess skin pigmentation impact on measurement accuracy. ΔStO2 (mean ± SD) progressively decreased with increasing Fitzpatrick score: 14.0 ± 22.98 for score 1, 6.87 ± 17.45 for score 2, −3.13 ± 6.89 for score 3, and −40.75 ± 22.27 for score 5, indicating a shift from positive to negative O2 change. Fitzpatrick scores significantly correlated with ΔStO2 (ρ = −0.392, p = 0.016). ANOVA confirmed differences (p = 0.008), with Tukey’s post hoc testing showing significant differences between Fitzpatrick scores 1 and 5 (p = 0.022), and 2 and 5 (p = 0.006). SnapshotNIR technology demonstrated measurable sensitivity for detecting changes in StO2 and predicting ischemia; however, NIRS-based devices may overestimate oxygenation in darker skin pigmentation, highlighting a need for device calibration to improve accuracy across skin tones.
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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".