The Utility of near Infrared Imaging in Intra-Operative Prediction of Flap Outcome: A Reverse McFarlane Skin Flap Model Study
Bibliographic record
Abstract
Skin flaps are complex procedures used extensively in reconstructive surgery that require post-operative monitoring to ensure that they do not fail. Near infrared (NIR) spectroscopic imaging is a convenient, non-invasive method for surgeons to examine flaps during surgery and in the early post-operative period. Using a reverse McFarlane skin flap model, we show that model-free chemometric methods as well as simple modified Beer-Lambert analysis of the NIR images provide insights into the blood supply to flaps and demonstrate that the technique can detect and localise perfusion-related complications as well as give real-time feedback to the surgeon as they try to resolve the complication. We also show that using estimates of tissue haemoglobin oxygen saturation, imaging measurements made during surgery and in the early post-operative period are highly predictive of the outcome of the flap tissue with specificities and sensitivities exceeding 85%.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".