The Temporalis Muscle Flap: A Useful Adjunct in Reconstruction of Combined Defects of the Upper and Lower Eyelids
Bibliographic record
Abstract
BACKGROUND: Eyelid reconstruction following oncological resection remains a challenge. Multiple techniques have been described for isolated upper or lower eyelid defects. OBJECTIVE: To describe the use of the temporalis flap for reconstruction of an eyelid defect involving both the upper and lower eyelids. METHODS: Excision of a basal cell carcinoma was performed. This resulted in full-thickness defects of the upper and lower eyelids, the lateral canthus including upper and lower canthal tendons, the upper right mid-face, temple, lateral two-thirds of the eyebrow and forehead. The tumour was found to be adherent to bone and dissecting deeply into the lateral orbital cavity as well as along the orbital roof. RESULTS: The lateral orbital rim was reconstructed using the prebent titanium mesh implant. A temporalis muscle flap allowed for draping over the reconstructed orbital rim and to provide reconstruction of the tarsal plates of the upper and lower eyelids. The remaining large cutaneous defect, which involved more than half of the lower eyelid, was reconstructed using a large Mustarde cervical facial rotation flap. CONCLUSIONS: The temporalis muscle flap provides abundant well vascularized tissue and has been described for head and neck reconstruction. A novel technique allows reconstruction of both the upper and lower eyelids using the temporalis muscle in combination with local flaps.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".