Outcomes of free flap reconstructions with near‐infrared spectroscopy (NIRS) monitoring: A systematic review
Bibliographic record
Abstract
BACKGROUND: Free flap failure or vascular compromise remains a dreadful complication of microvascular free tissue transfer. Near-infrared spectroscopy (NIRS) is a novel technique for free flap monitoring that has the propensity for early detection of vascular compromise when compared to the current gold standard, clinical monitoring (CM). The objective of this review is to evaluate the efficacy of a NIRS system in the postoperative monitoring of free flaps and its effect on flap salvage. METHODS: A comprehensive literature review was performed including English-language articles evaluating the use of NIRS in free flap monitoring. MEDLINE, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), OVID, and Web of Science were searched upto December 2017. RESULTS: A total of 590 articles were identified, and 10 articles were included for analysis. Overall, flaps with vascular compromise monitored with NIRS had a significantly higher salvage rate of 89% compared with a salvage rate of 50% in the flaps monitored by CM alone (p < .01). Partial loss occurred in 15% of the successful salvages in the NIRS group versus 80% with CM alone (p < .01). Detection of vascular compromise by NIRS preceded clinical signs on average by 82 ± 49 min. NIRS was accurate in detecting compromised flaps with a low false-positive and false-negative rate. CONCLUSION: Despite lack of robust data, NIRS has the potential to be an objective, accurate, and continuous postoperative free flap monitoring technique with a greater flap salvage rate than CM alone.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".