Composite submental flaps in facial reconstructive surgery involving the zygoma and orbit
Bibliographic record
Abstract
BACKGROUND: The submental island flap (SIF) is a reliable option for reconstructing defects in the facial region and offers several advantages when compared to free-flap alternatives. While the reconstructive applications of the SIF have been demonstrated in the lower face, there are limited reports on its utility as a composite flap for reconstructing defects of the upper facial skeleton. To our knowledge, we report the first cases of composite (osteocutaneous) SIFs used for reconstruction of complex facial defects involving the zygoma and lateral orbit respectively. CASE PRESENTATIONS: Three consecutive cases are presented. All were performed following resection of skin cancers with invasion of the upper facial skeleton. The first case was a 68-year-old male with a longstanding history of non-melanoma skin cancers who presented with a 7 cm recurrent basal cell carcinoma (BCC) with bicortical invasion of the left zygoma. The second case was an 88-year-old female with several squamous cell carcinomas (SCC), including a dominant 7.1 cm SCC on the right temple with orbital invasion. A third case was a 75-year-old immunosuppressed male with a 6.5 cm SCC of the right cheek with invasion of the orbit and zygoma following prior resection as well as high dose radiotherapy. The operative management of all cases involved harvesting the SIF on its vascular pedicle alongside the inferior portion of the mandible with rigid fixation to address the bony defects. The first case was robust throughout adjuvant radiotherapy with no flap complications after 2 year follow up. The second patient received adjuvant radiation therapy to an area that was previously radiated. Although the flap remained viable for a year, the patient experienced delayed soft tissue loss over the bony segment and eventual devitalization of the distal flap. The third case achieved a satisfactory result with no complications. CONCLUSIONS: Our case series outlines a unique application of the composite (osteocutaneous) submental island flap (SIF) for reconstruction of complex facial defects involving the upper facial skeleton. The osteocutaneous SIF should be used with caution in patients receiving adjuvant radiotherapy who have a history of previous radiation to the same or overlapping field.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".