To Divide or Not to Divide? A Case Report Using Near-Infrared Spectroscopy to Assist With Decision Making on Timing of Division of Forehead Flaps
Bibliographic record
Abstract
Paramedian forehead flaps (PMFFs) are commonly performed procedures for the reconstruction of complex nasal defects and require multiple stages predicated on when the interpolated pedicle is no longer required to provide perfusion to the neo-nose. As the presence of the pedicle is disfiguring, the flap is therefore divided ideally as soon as safely possible, but that determination is challenging. Recently, a novel device that uses near-infrared spectroscopy (NIRS) to measure tissue oxygen saturation has been developed and may provide an accurate and cost-effective tool to assess tissue perfusion. Here we present the use of this technology in the management of 2 patients who underwent successful oncologic nasal reconstructions using PMFFs. Using the device as a clinical adjunct, we measured tissue oxygen saturation values in the flaps before and after pedicle division, which assisted us in deciding when to safely divide the flaps, as well as with post-division management, particularly when a low tissue oxygen saturation reading ultimately necessitated the use of nitroglycerine past to improve flap perfusion in one of our patients. Interestingly, the values we recorded corresponded well to previously published cut-off values for adequate tissue perfusion, which have to date only been reported for free flaps. To our knowledge, this is the first description of the use of NIRS in the division of PMFFs, which we overall found to be a useful and reliable adjunct to a clinical examination in assessing flap neovascularization.
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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 | 0.001 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".