Intraoperative Indocyanine Green Angiography for Assessing Flap Perfusion in Skull Base Reconstruction: A Systematic Review
Bibliographic record
Abstract
Abstract Objective This study was aimed to study the current use of intraoperative indocyanine green (ICG) angiography during skull base reconstruction and understand its efficacy in predicting postoperative magnetic resonance imaging (MRI) enhancement and flap. Study Design The Embase, the Cochrane Central Register of Controlled Trials (CENTRAL), Web of Science, and Google Scholar databases were searched from the date of inception until August 2020 for studies of ICG flap perfusion assessment during skull base reconstruction. The primary outcome of interest was the development of cerebrospinal fluid (CSF) leak after skull base reconstruction. Secondary outcomes of interest included postoperative meningitis, flap MRI enhancement, flap necrosis, flap perfusion measurements, and total complications. Results Search results yielded 189 studies, from which seven studies with a total of 104 patients were included in the final analysis. There were 44 nasoseptal flaps (NSF), two lateral nasal wall flaps (LNWF), 14 pericranial flaps (PCF), and 44 microvascular free flaps. The rates of CSF leak and postoperative MRI enhancement were 11 and 94%, respectively. There was one case of postoperative meningitis. Pooled analysis of the available data showed that intraoperative ICG flap perfusion was associated with flap enhancement on postoperative MRI (p = 0.008) and CSF leak (p = 0.315) by Fisher's exact test. Conclusion The available literature suggests intraoperative ICG enhancement is associated with postoperative MRI enhancement. Given the small sample sizes in the literature and the rarity of complications associated with skull base reconstruction, intraoperative ICG enhancement has not been predictive of flap necrosis or postoperative complications such as CSF leak or meningitis. Level of Evidence This study presents level 3 evidence as a systematic review of case studies, case reports, and retrospective and prospective trials with no blinding, controls, and inconsistently applied reference standards.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.006 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 teacher head, 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".